ObjectiveTo build a supervised machine learning-based classifier, which can accurately predict whether Tai Chi practitioners may experience knee pain after years of exercise. DesignA prospective approach was used. Data were collected using face-to-face through a self-designed questionnaire. SettingSingle centre in Shanghai, China. ParticipantsA total of 1750 Tai Chi practitioners with a course of Tai Chi exercise over 5 years were randomly selected. MeasuresAll participants were measured by a questionnaire survey including personal information, Tai Chi exercise pattern and Irrgang Knee Outcome Survey Activities of Daily Living Scale. The validity of the questionnaire was analysed by logical analysis and test, and the reliability of this questionnaire was mainly tested by a re-test method. Dataset 1 was established by whether the participant had knee pain, and dataset 2 by whether the participant's knee pain affected daily living function. Then both datasets were randomly assigned to a training and validating dataset and a test dataset in a ratio of 7:3. Six machine learning algorithms were selected and trained by our dataset. The area under the receiver operating characteristic curve was used to evaluate the performance of the trained models, which determined the best prediction model. ResultsA total of 1703 practitioners completed the questionnaire and 47 were eliminated for lack of information. The total reliability of the scale is 0.94 and the KMO (Kaiser-Meyer-Olkin measure of sampling adequacy) value of the scale validity was 0.949 (>0.7). The CatBoost algorithm-based machine-learning model achieved the best predictive performance in distinguishing practitioners with different degrees of knee pain after Tai Chi practice. 'Having knee pain before Tai Chi practice', 'knee joint warm-up' and 'duration of each exercise' are the top three factors associated with pain after Tai Chi exercise in the model. 'Having knee pain before Tai Chi practice', 'Having Instructor' and 'Duration of each exercise' were most relevant to whether pain interfered with daily life in the model. ConclusionCatBoost-based machine learning classifier accurately predicts knee pain symptoms after practicing Tai Chi. This study provides an essential reference for practicing Tai Chi scientifically to avoid knee pain.

BackgroundEffective psychotherapy should satisfy the client, but that satisfaction depends on many factors. We do not fully understand the factors that affect client satisfaction with psychotherapy and how these factors synergistically affect a client’s psychotherapy experience.AimsThis study aims to use machine learning to predict Chinese clients’ satisfaction with psychotherapy and analyze potential outcome contributors.MethodsIn this cross-sectional investigation, a self-compiled online questionnaire was delivered through the WeChat app. The information of 791 participants who had received psychotherapy was used in the study. A series of features, for example, the participants’ demographic features and psychotherapy-related features, were chosen to distinguish between participants satisfied and dissatisfied with the psychotherapy they received. With our dataset, we trained seven supervised machine-learning-based algorithms to implement prediction models.ResultsAmong the 791 participants, 619 (78.3%) reported being satisfied with the psychotherapy sessions that they received. The occupation of the clients, the location of psychotherapy, and the form of access to psychotherapy are the three most recognizable features that determined whether clients are satisfied with psychotherapy. The machine-learning model based on the CatBoost achieved the highest prediction performance in classifying satisfied and psychotherapy clients with an F1 score of 0.758.ConclusionThis study clarified the factors related to clients’ satisfaction with psychotherapy, and the machine-learning-based classifier accurately distinguished clients who were satisfied or unsatisfied with psychotherapy. These results will help provide better psychotherapy strategies for specific clients, so they may achieve better therapeutic outcomes.

Objective: Side effects in the psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them.Methods: We designed the Psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients' side effects. A number of features were selected to distinguish the therapists by category. Six machine learning-based algorithms were selected and trained by our dataset to build classification models. We leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories.Results: Our study demonstrated the following: (1) Of the therapists, 316 perceived clients' side effects in psychotherapy, with a incidence of 59.6%; the most common type was "make the clients or patients feel bad" (49.8%). (2) A Random Forest-based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients' side effects, with an F1 score of 0.722 and an AUC value of 0.717. (3) "Therapists' psychological activity" was the most relevant feature for distinguishing the therapist category.Conclusions: Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their psychological states, was the most critical factor in predicting the therapist's perception of the side effects of psychotherapy.

Background: Side effects in psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. Methods: We designed the Psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients’ side effects. A number of features were selected to distinguish the therapists by category. Six machine learning–based algorithms were selected and trained by our dataset to build classification models. To make the prediction model interpretable, we leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories. Results: Our study demonstrated the following: 1) Of the therapists, 316 perceived the side effects of the clients in the ongoing psychotherapy sessions, with a 59.6% incidence of side effects. Among all 7 perception types of the side effects, the most common type was “make the clients or patients feel bad” (49.8%). 2) A random forest–based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients’ side effects, with an F1 score of 0.722 and an AUC value of 0.717. 3) When “therapists’ psychological activity” was considered a possible cause of the side effects in psychotherapy by the therapists, it was the most relevant feature for distinguishing the therapist category. Conclusions: Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their own psychological states, was the most important factor in predicting the therapist's perception of the side effects of psychotherapy.

Side effects in psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. Using machine learning techniques to distinguish therapists with and without the perception of consulting side effects, and identify the predictive factors of therapists who could perceive client side effects in psychotherapy. We designed the Psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients’ side effects. A number of features were selected to distinguish the therapists by category. Six machine learning–based algorithms were selected and trained by our dataset to build classification models. To make the prediction model interpretable, we leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories. Our study demonstrated the following: 1) Of the therapists, 316 perceived the side effects of the clients in the ongoing psychotherapy sessions, with a 59.6% incidence of side effects. Among all 7 perception types of the side effects, the most common was “make the clients or patients feel bad” (49.8%). 2) A random forest–based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients’ side effects, with an F1 score of 0.722 and an AUC value of 0.717. 3) When “therapists’ psychological activity” was considered a possible cause of the side effects in psychotherapy by the therapists, it was the most relevant feature for distinguishing the therapist category. Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their own psychological states, was the most important factor in predicting the therapist's perception of the side effects of psychotherapy.

The Coronavirus Disease 2019 pandemic has changed the way most people live and work, including the way in which students undertake study. To protect students during the pandemic, most schools in China adopted a study-at-home model. Under these circumstances, the pathophysiology teaching team at Tongji University considered how to reform teaching methods to minimize the impact of the pandemic on students’ curriculum studies. This article describes our teaching reforms in detail, notably a combination of online education resources, online discussion courses, and a WeChat study group. We compared the effects of the reformed and traditional teaching approaches, including student performance and student evaluation of the reformed teaching methods. Analysis showed that although students were generally worried about the impact of the pandemic on their curriculum studies, their overall performance was not affected by the reformed teaching methods. Of interest, compared with traditional teaching, the proportion of students with higher final test scores (≥90 points) actually increased. The revised teaching methods promoted the learning of some students externally and internally and enhanced their enthusiasm for medical study and their academic performance. These approaches could be applied as a reference for future course arrangements after the pandemic.

Background: Side effects in psychotherapy are a common phenomenon, but due to insufficient understanding of the relevant predictors of side effects in psychotherapy, many psychotherapists or clinicians fail to identify and manage these side effects. The purpose of this study was to predict whether clients or patients would experience side effects in psychotherapy by machine learning and to analyze the related influencing factors. Methods: A self-compiled “Psychotherapy Side Effects Questionnaire (PSEQ)” was delivered online by a WeChat official account. Three hundred and seventy participants were included in the cross-sectional analysis. Psychotherapy outcomes were classified as participants with side effects and without side effects. A number of features were selected to distinguish participants with different psychotherapy outcomes. Six machine learning-based algorithms were then chosen and trained by our dataset to build outcome prediction classifiers. Results: Our study showed that: (1) the most common side effects were negative emotions in psychotherapy, such as anxiety, tension, sadness, and anger, etc. (24.6%, 91/370); (2) the mental state of the psychotherapist, as perceived by the participant during psychotherapy, was the most relevant feature to predict whether clients would experience side effects in psychotherapy; (3) a Random Forest-based machine learning classifier offered the best prediction performance of the psychotherapy outcomes, with an F1-score of 0.797 and an AUC value of 0.804. These numbers indicate a high prediction performance, which allowed our approach to be used in practice. Conclusions: Our Random Forest-based machine learning classifier could accurately predict the possible outcome of a client in psychotherapy. Our study sheds light on the influencing factors of the side effects of psychotherapy and could help psychotherapists better predict the outcomes of psychotherapy.

Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy. Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study. Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature “age at seizure onset” with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD. Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.

The rapid replenishment of release-ready synaptic vesicles (SVs) at a limiting number of presynaptic release sites is required to sustain high-frequency neurotransmission in CNS neurons. Failure to clear release sites from previously exocytosed material has been shown to impair vesicle replenishment and, therefore, fast neurotransmission. The identity of this material and the machinery that removes it from release sites have remained enigmatic. Here we show that the endocytic scaffold protein intersectin 1 clears release sites by direct SH3 domain-mediated association with a non-canonical proline-rich segment of synaptobrevin assembled into the SNARE complex for neuroexocytosis. Acute structure-based or sustained genetic interference with SNARE complex recognition by intersectin 1 causes a rapid stimulation frequency-dependent depression of neurotransmission due to impaired replenishment of release-ready SVs. These findings identify a key molecular mechanism that underlies exo-endocytic coupling during fast neurotransmitter release at central synapses.

Objective: The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy. Methods: We collected information from 287 patients with newly diagnosed epilepsy between 2009 and 2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at least 3 years. A number of features, including demographic features, medical history, and auxiliary examinations (electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission is further divided into early remission and late remission. Five classical machine learning algorithms, i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and trained by our dataset to get classification models. Results: Our study shows that 1) compared with the other four algorithms, the XGBoost algorithm based machine learning model achieves the best prediction performance of the AED treatment outcomes between remission and never remission patients with an F1 score of 0.947 and an area under the curve (AUC) value of 0.979: 2) The best discriminative factor for remission and never remission patients is higher number of seizures before treatment (>3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the highest dependence to the categories of early and late remission patients. Significances: Our XGBoost-based machine learning classifier accurately predicts the most probable AED treatment outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The classifier's prediction result could help disease guide counseling and eventually improve treatment strategies. (C) 2019 Elsevier Inc. All rights reserved.

We identified a family in which a translocation between chromosomes X and 14 was associated with cognitive impairment and a complex genetic disorder termed "Genetic Epilepsy and Febrile Seizures Plus" (GEFS(+)). We demonstrate that the breakpoint on the X chromosome disrupted a gene that encodes an auxiliary protein of voltage-gated Na+ channels, fibroblast growth factor 13 (Fgf13). Female mice in which one Fgf13 allele was deleted exhibited hyperthermia-induced seizures and epilepsy. Anatomic studies revealed expression of Fgf13 mRNA in both excitatory and inhibitory neurons of hippocampus. Electrophysiological recordings revealed decreased inhibitory and increased excitatory synaptic inputs in hippocampal neurons of Fgf13 mutants. We speculate that reduced expression of Fgf13 impairs excitability of inhibitory interneurons, resulting in enhanced excitability within local circuits of hippocampus and the clinical phenotype of epilepsy. These findings reveal a novel cause of this syndrome and underscore the powerful role of FGF13 in control of neuronal excitability.

AIMS/HYPOTHESIS:Insulin is a key metabolic regulator in health and diabetes. In pancreatic beta cells, insulin release is regulated by the major second messengers Ca(2+) and cAMP: exocytosis is triggered by Ca(2+) and mediated by the cAMP/protein kinase A (PKA) signalling pathway. However, the causal link between these two processes in primary beta cells remains undefined.METHODS:Time-resolved confocal imaging of fluorescence resonance energy transfer signals was performed to visualise PKA activity, and combined membrane capacitance recordings were used to monitor insulin secretion from patch-clamped rat beta cells.RESULTS:Membrane depolarisation-induced Ca(2+) influx caused an increase in cytosolic PKA activity via activating a Ca(2+)-sensitive adenylyl cyclase 8 (ADCY8) subpool. Glucose stimulation triggered coupled Ca(2+) oscillations and PKA activation. ADCY8 knockdown significantly reduced the level of depolarisation-evoked PKA activation and impaired replenishment of the readily releasable vesicle pool. Pharmacological inhibition of PKA by two inhibitors reduced depolarisation-induced PKA activation to a similar extent and reduced the capacity for sustained vesicle exocytosis and insulin release.CONCLUSIONS/INTERPRETATION:Our findings suggest that depolarisation-induced Ca(2+) influx plays dual roles in regulating exocytosis in rat pancreatic beta cells by triggering vesicle fusion and replenishing the vesicle pool to support sustained insulin release. Therefore, Ca(2+) influx may be important for glucose-stimulated insulin secretion.

Sustained fast neurotransmission requires the rapid replenishment of release-ready synaptic vesicles (SVs) at presynaptic active zones. Although the machineries for exocytic fusion and for subsequent endocytic membrane retrieval have been well characterized, little is known about the mechanisms underlying the rapid recruitment of SVs to release sites. Here we show that the Down syndrome-associated endocytic scaffold protein intersectin 1 is a crucial factor for the recruitment of release-ready SVs. Genetic deletion of intersectin 1 expression or acute interference with intersectin function inhibited the replenishment of release-ready vesicles, resulting in short-term depression, without significantly affecting the rate of endocytic membrane retrieval. Acute perturbation experiments suggest that intersectin-mediated vesicle replenishment involves the association of intersectin with the fissioning enzyme dynamin and with the actin regulatory GTPase CDC42. Our data indicate a role for the endocytic scaffold intersectin in fast neurotransmitter release, which may be of prime importance for information processing in the brain.

Although Ca(2+)/calmodulin has been suggested to play a role during endocytosis, it remains unknown if binding of Ca(2+) to calmodulin is essential for initiating endocytosis or if this interaction only has a modulatory effect on endocytosis. In this study, using time-resolved capacitance measurements at the rat calyx of Held synapse, the role of calmodulin in endocytosis was examined. Our results demonstrate that blocking calmodulin with an inhibitory peptide, which interfers with the binding of calmodulin to downstream targets, slowed the rate of endocytosis, but only when accompanied by high Ca(2+) influx. In response to a short train of action potential-like stimulation, blocking calmodulin had no effect on endocytosis. Furthermore, we have identified conditions in which inhibition of calmodulin fails to affect the rate of endocytosis, but nevertheless retards recruitment of synaptic vesicles to the fast-releasing vesicle pool responsible for synchronous release. The results indicate that calmodulin facilitates endocytosis in an activity-dependent manner but is not mandatory for endocytosis, and suggest that calmodulin modulates an endocytotic intermediate process, which in turn affects synaptic vesicle recruitment and membrane fission.

cAMP potentiates neurotransmitter release from the presynaptic terminal in many CNS synapses, but the underlying mechanisms remain unclear. Here we addressed this issue quantitatively by performing double patch-clamp recordings from the pre- and postsynaptic compartments of the calyx of Held synapse in rat brain stem slices in combination with Ca(2+) uncaging. We found that elevation of cAMP increased intracellular Ca(2+) sensitivity for transmitter release especially at lower Ca(2+) concentrations. The change in Ca(2+) sensitivity was limited to the fast-releasing synaptic vesicles, which could be released rapidly on action potentials. cAMP did not affect the slowly releasing vesicles. Fit of the data using a simplified allosteric model indicated that cAMP increased the fusion "willingness," thereby facilitating transmitter release. We suggest that synaptic vesicles have to be positionally primed to the release sites close to the Ca(2+) channel cluster for cAMP to modulate intracellular Ca(2+) sensitivity of transmitter release.

The hyperpolarization-activated, cyclic nucleotide-gated (HCN) channels, or cardiac (I(f))/neuronal (I(h)) time- and voltage-dependent inward cation current channels, are conventionally considered as monovalent-selective channels. Recently we discovered that calcium ions can permeate through HCN4 and I(h) channels in neurons. This raises the possibility of Ca(2+) permeation in I(f), the I(h) counterpart in cardiac myocytes, because of their structural homology. We performed simultaneous measurement of fura-2 Ca(2+) signals and whole cell currents produced by HCN2 and HCN4 channels (the 2 cardiac isoforms present in ventricles) expressed in HEK293 cells and by I(f) in rat ventricular myocytes. We observed Ca(2+) influx when HCN/I(f) channels were activated. Ca(2+) influx was increased with stronger hyperpolarization or longer pulse duration. Cesium, an I(f) channel blocker, inhibited I(f) and Ca(2+) influx at the same time. Quantitative analysis revealed that Ca(2+) flux contributed to approximately 0.5% of current produced by the HCN2 channel or I(f). The associated increase in Ca(2+) influx was also observed in spontaneously hypertensive rat (SHR) myocytes in which I(f) current density is higher than that of normotensive rat ventricle. In the absence of EGTA (a Ca(2+) chelator), preactivation of I(f) channels significantly reduced the action potential duration, and the effect was blocked by another selective I(f) channel blocker, ZD-7288. In the presence of EGTA, however, preactivation of I(f) channels had no effects on action potential duration. Our data extend our previous discovery of Ca(2+) influx in I(h) channels in neurons to I(f) channels in cardiac myocytes.

The locus coeruleus (LC) harbors a compact group of noradrenergic cell bodies projecting to virtually all parts of the central nervous system. By using combined measurements of amperometry and patch-clamp, quantal vesicle release of noradrenaline (NA) was detected as amperometric spikes, after depolarization of the LC neurons. After a pulse depolarization, the average latency of amperometric spikes was 1,870 ms, whereas the latency of glutamate-mediated excitatory postsynaptic currents was 1.6 ms. A substantial fraction of the depolarization-induced amperometric spikes originated from the somata. In contrast to glutamate-mediated excitatory postsynaptic currents, NA secretion was strongly modulated by the action potential frequency (0.5-50 Hz). Somatodendritic NA release from LC upon enhanced cell activity produced autoinhibition of firing and of NA release. We conclude that, in contrast to classic synaptic transmission, quantal NA release from LC somata is characterized by a number of distinct properties, including long latency and high sensitivity to action potential frequency.

Aim: Ca 2+ release from the endoplasmic reticulum (ER) is an integral component of neuronal Ca 2+ signaling. The present study is to investigate properties of local Ca 2+ release events in superior cervical ganglion (SCG) neurons. Methods: Primary cultured SCG neurons were prepared from neonatal rats (P3-P7). Low concentration of caffeine was used to induce Ca 2+ release from the ER Ca 2+ store, and intracellular Ca 2+ was recorded by high-resolution line scan confocal imaging and the Ca 2+ indicator Fluo-4. Results: Two populations of local Ca 2+ release events with distinct temporal characteristics were evoked by 1.5 mmol/L caffeine near the surface membrane in the soma and the neurites of SCG neurons. Brief events similar to classic Ca 2+ sparks lasted a few hundreds of milliseconds, whereas long-lasting events displayed duration up to tens of seconds. Typical somatic and neurite sparks were of 0.3- and 0.52-fold increase in local Fluo-4 fluorescence, respectively. Typical Ca 2+ glows were brighter (Δ F/F 0 approximately 0.6), but were highly confined in space. The half maximum of full duration of neurite sparks was much longer than those in the soma (685 vs 381 ms). Conclusion: Co-existence of Ca 2+ sparks and Ca 2+ glows in SCG neurons indicates distinctive local regulation of Ca 2+ release kinetics. The local Ca 2+ signals of variable, site-specific temporal length may bear important implications in encoding a “memory” of the trigger signal.

Aim: Ca 2+ release from the endoplasmic reticulum (ER) is an integral component of neuronal Ca 2+ signaling. The present study is to investigate properties of local Ca 2+ release events in superior cervical ganglion (SCG) neurons. Methods : Primary cultured SCG neurons were prepared from neonatal rats (P3-P7). Low concentration of caffeine was used to induce Ca 2+ release from the ER Ca 2+ store, and intracellular Ca 2+ was recorded by high-resolution line scan confocal imaging and the Ca 2+ indicator Fluo-4. Results : Two populations of local Ca 2+ release events with distinct temporal characteristics were evoked by 1.5 mmol/L caffeine near the surface membrane in the soma and the neurites of SCG neurons. Brief events similar to classic Ca 2+ sparks lasted a few hundreds of milliseconds, whereas long-lasting events displayed duration up to tens of seconds. Typical somatic and neurite sparks were of 0.3- and 0.52-fold increase in local Fluo-4 fluorescence, respectively. Typical Ca 2+ glows were brighter (Δ F/F 0 approximately 0.6), but were highly confined in space. The half maximum of full duration of neurite sparks was much longer than those in the soma (685 vs 381 ms). Conclusion : Co-existence of Ca 2+ sparks and Ca 2+ glows in SCG neurons indicates distinctive local regulation of Ca 2+ release kinetics. The local Ca 2+ signals of variable, site-specific temporal length may bear important implications in encoding a “memory” of the trigger signal.

A novel "long chain" toxin BmP09 has been purified and characterized from the venom of the Chinese scorpion Buthus martensi Karsch. The toxin BmP09 is composed of 66 amino acid residues, including eight cysteines, with a mass of 7721.0 Da. Compared with the B. martensi Karsch AS-1 as a Na+ channel blocker (7704.8 Da), the BmP09 has an exclusive difference in sequence by an oxidative modification at the C terminus. The sulfoxide Met-66 at the C terminus brought the peptide a dramatic switch from a Na+ channel blocker to a K+ channel blocker. Upon probing the targets of the toxin BmP09 on the isolated mouse adrenal medulla chromaffin cells, where a variety of ion channels coexists, we found that the toxin BmP09 specifically blocked large conductance Ca2+- and voltage-dependent K+ channels (BK) but not Na+ channels at a range of 100 nM concentration. This was further confirmed by blocking directly the BK channels encoded with mSlo1 alpha-subunits in Xenopus oocytes. The half-maximum concentration EC50 of BmP09 was 27 nM, and the Hill coefficient was 1.8. In outside-out patches, the 100 nM BmP09 reduced similar to 70% currents of BK channels without affecting the single-channel conductance. In comparison with the "short chain" scorpion peptide toxins such as Charybdotoxin, the toxin BmP09 behaves much better in specificity and reversibility, and thus it will be a more efficient tool for studying BK channels. A three-dimensional simulation between a BmP09 toxin and an mSlo channel shows that the Lys-41 in BmP09 lies at the center of the interface and plugs into the entrance of the channel pore. The stable binding between the toxin BmP09 and the BK channel is favored by aromatic pi-pi interactions around the center.