End-to-end multimodal emotion recognition based on facial expressions and remote photoplethysmography signals.

Emotion is a complex physiological phenomenon, and a single modality may be insufficient for accurately determining human emotional states. This paper proposes an end-to-end multimodal emotion recognition method based on facial expressions and non-contact physiological signals. Facial expression features and remote photoplethysmography (rPPG) signals are extracted from facial video data, and a transformer-based cross-modal attention mechanism (TCMA) is used to learn the correlation between the two modalities. The results show that the accuracy of emotion recognition can be slightly improved by combining facial expressions with accurate rPPG signals. The performance is further improved with the use of TCMA, for which the binary classification accuracy of valence and arousal is 91.11% and 90.00%, respectively. Additionally, when experiments are conducted using the whole dataset, an increased accuracy of 7.31% and 4.23% for the binary classification of valence and arousal, and an improved accuracy of 5.36% for the four classifications of valence-arousal are achieved when TCMA is used in modal fusion, compared to using only facial expression modality, which fully demonstrates the effectiveness and robustness of TCMA. This method makes it possible to realize multimodal emotion recognition of facial expressions and contactless physiological signals in reality.

Automatically detecting OSAHS patients based on transfer learning and model fusion.

Objective. Snoring is the most typical symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) that can be used to develop a non-invasive approach for automatically detecting OSAHS patients.Approach. In this work, a model based on transfer learning and model fusion was applied to classify simple snorers and OSAHS patients. Three kinds of basic models were constructed based on pretrained Visual Geometry Group-16 (VGG16), pretrained audio neural networks (PANN), and Mel-frequency cepstral coefficient (MFCC). The XGBoost was used to select features based on feature importance, the majority voting strategy was applied to fuse these basic models and leave-one-subject-out cross validation was used to evaluate the proposed model.Main results. The results show that the fused model embedded with top-5 VGG16 features, top-5 PANN features, and MFCC feature can correctly identify OSAHS patients (AHI > 5) with 100% accuracy.Significance. The proposed fused model provides a good classification performance with lower computational cost and higher robustness that makes detecting OSAHS patients at home possible.

Peripheral blood lymphocyte subsets predict the efficacy of TACE with or without PD-1 inhibitors in patients with hepatocellular carcinoma: a prospective clinical study.

Background: Although peripheral blood lymphocyte subsets, particularly PD-1+ T cells, are promising prognostic indicators for patients with cancer. However, their clinical significance remains unclear. Methods: We prospectively enrolled 157 patients with hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolization combined with or without PD-1 inhibitors. Twenty peripheral lymphocyte subsets and cytokines were analyzed. We analyzed the differences in PD-1+ T cells between patients treated with and without PD-1 inhibitors and their associations with tumor response, survival prognosis, and clinical features. Results: We found that the baseline CD8+PD-1+ and CD4+PD-1+ T-cell frequencies in patients who had received PD-1 inhibitors were lower than those in patients who had not received PD-1 inhibitors (p < 0.001). In the former patients, there were no differences in PD-1+ T-cell frequencies between the responder and non-responder subgroups (p > 0.05), whereas in the latter patients, the levels of CD8+PD-1+ T cells, CD4+PD-1+ T cells, and CD8+PD-1+/CD4+PD-1+ ratio did not predict tumor response, progression-free survival (PFS), or overall survival (OS) (p>0.05). Furthermore, in multivariate analysis of patients treated with or without PD-1 inhibitors revealed that the levels of CD8+CD38+ T cells (OR = 2.806, p = 0.006) were associated with tumor response, whereas those of CD8+CD28+ T cells (p = 0.038, p = 0.001) and natural killer (NK) cells (p = 0.001, p = 0.027) were associated with PFS and OS. Although, these independent prognostic factors were associated with progressive tumor characteristics (p<0.05), with the exception of CD8+CD28+ T cells, changes in these factors before and after treatment were unassociated with tumor response (p > 0.05). Conclusion: Circulating CD8+CD38+ T cells, CD8+CD28+ T cells, and NK cells were identified as potential prognostic factors for tumor response and survival in patients with HCC. Contrastingly, although PD-1 inhibitors can effectively block the T cell PD-1 receptor, the baseline PD-1+ T-cell frequencies and changes in the frequency of these cells have limited prognostic value.

Identification of OSAHS patients based on ReliefF-mRMR feature selection.

Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a serious chronic sleep disorder. Snoring is a common and easily observable symptom of OSAHS patients. The purpose of this work is to identify OSAHS patients by analyzing the acoustic characteristics of snoring sounds throughout the entire night. Ten types of acoustic features, such as Mel-frequency cepstral coefficients (MFCC), linear prediction coefficients (LPC) and spectral entropy among others, were extracted from the snoring sounds. A fused feature selection algorithm based on ReliefF and Max-Relevance and Min-Redundancy (mRMR) was proposed for optimal feature set selection. Four types of machine learning models were then applied to validate the effectiveness of OSAHS patient identification. The results show that the proposed feature selection algorithm can effectively select features with high contribution, including MFCC and LPC. Based on the selected top-20 features and using a support vector machine model, the accuracies in identifying OSAHS patients under the thresholds of AHI = 5,15, and 30, were 100%, 100%, and 98.94%, respectively. This indicates that the proposed model can effectively identify OSAHS patients.

Tunable and switchable multi-wavelength pulsed fiber laser based on a nonlinear optical loop mirror and an improved Sagnac filter.

We propose and experimentally demonstrate a tunable and switchable multi-wavelength erbium-doped fiber ring pulsed laser based on a nonlinear optical loop mirror (NOLM) and an improved Sagnac filter. To achieve multi-wavelength pulsed laser output, we adopt a NOLM as a quasi-saturable absorber and an improved Sagnac loop as a wavelength selected filter. The constructed laser has a maximum output wavelength number of five with a pulse repetition frequency of 40.45 kHz and pulse duration of 108 ns. The laser can output single-wavelength and dual-wavelength pulsed lasers within a certain wavelength tuning range and a five-wavelength pulsed laser with a constant wavelength interval of 3 nm by adjusting the polarization controller. Dual-wavelength, three-wavelength, and four-wavelength pulsed lasers with various wavelength intervals are also obtained. The output performance of the constructed laser is tested with a maximum average output power of 127.45 µW and minimum pulse duration of 52 ns, and the stability of the laser output is also tested with a maximum power fluctuation of 0.62 dB and minimum wavelength drift of 0.51 nm with pump power of 350 mW.

[Prokaryotic expression, polyclonal antibody preparation, spatio-temporal expression profile and functional analysis of c-Myc of Helicoverpa armigera (Lepidoptera: Noctuidae)].

c-Myc protein encoded by c-Myc (cellular-myelocytomatosis viral oncogene) gene regulates the related gene expression through the Wnt/ß-catenin signaling pathway, and has received extensive attention in recent years. The purpose of this study was to express Helicoverpa armigera c-Myc gene (Ha-c-Myc) by using prokaryotic expression system, prepare the polyclonal antibody, examine the spatio-temporal expression profile of Ha-c-Myc, and investigate the possible function of Ha-c-Myc in regulating H. armigera sterol carrier protein-2 (SCP-2) gene expression. The Ha-c-Myc gene was amplified by PCR and cloned into a prokaryotic expression plasmid pET-32a(+). The recombinant plasmid pET-32a-Ha-c-Myc was transformed into Escherichia coli BL21. IPTG was used to induce the expression of the recombinant protein. Protein was purified by Ni2+-NTA column and used to immunize New Zealand rabbits for preparing the polyclonal antibody. The Ha-c-Myc expression levels in different developmental stages (egg, larva, prepupa, pupa, and adult) of H. armigera and different tissues (midgut, fat body, head, and epidermis) of the prepupa were determined by real-time quantitative reverse transcription PCR (qRT-PCR). Ha-c-Myc siRNA was synthesized and transfected into H. armigera Ha cells. The relative mRNA levels of Ha-c-Myc and HaSCP-2 in Ha cells were detected by qRT-PCR. Results showed that the pET-32a-Ha-c-Myc recombinant plasmid was constructed. The soluble Ha-c-Myc protein of about 65 kDa was expressed in E. coli. The polyclonal antibody was prepared. Western blotting analysis suggested that the antibody had high specificity. Enzyme linked immunosorbent assay (ELISA) showed that the titer of the antibody was high. Ha-c-Myc gene expressed at all developmental stages, with high levels in the early and late instars of larva, and the prepupal stage. Tissue expression profiles revealed that Ha-c-Myc expressed in various tissues of prepupa, with high expression level in the midgut, but low levels in the epidermis and fat body. RNAi results showed that the knockdown of Ha-c-Myc expression significantly affected transcription of HaSCP-2, leading to a 50% reduction in HaSCP-2 mRNA expression level. In conclusion, the Ha-c-Myc was expressed through a prokaryotic expression system, and the polyclonal anti-Ha-c-Myc antibody was obtained. Ha-c-Myc may promote the expression of HaSCP-2 and play an important role in the lipid metabolism of H. armigera. These results may facilitate further study on the potential role and function mechanism of Ha-c-Myc in H. armigera and provide experimental data for exploring new targets of green pesticides.

AHI estimation of OSAHS patients based on snoring classification and fusion model.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a chronic and common sleep-breathing disease that could negatively influence lives of patients and cause serious concomitant diseases. Polysomnography(PSG) is the gold standard for diagnosing OSAHS, but it is expensive and requires overnight hospitalization. Snoring is a typical symptom of OSAHS. This study proposes an effective OSAHS screening method based on snoring sound analysis. Snores were labeled as OSAHS related snoring sounds and simple snoring sounds according to real-time PSG records. Three models were used, including acoustic features combined with XGBoost, Mel-spectrum combined with convolution neural network (CNN), and Mel-spectrum combined with residual neural network (ResNet). Further, the three models were fused by soft voting to detect these two types of snoring sounds. The subject's apnea-hypopnea index (AHI) was estimated according to these recognized snoring sounds. The accuracy and recall of the proposed fusion model achieved 83.44% and 85.27% respectively, and the predicted AHI has a Pearson correlation coefficient of 0.913 (R2 = 0.834, p < 0.001) with PSG. The results demonstrate the validity of predicting AHI based on analysis of snoring sound and show great potential for monitoring OSAHS at home.

Automatic identifying OSAHS patients and simple snorers based on Gaussian mixture models.

Objective.Snoring is a typical symptom of Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS). In this study, an effective OSAHS patient detection system based on snoring sounds is presented.Approach.The Gaussian mixture model (GMM) is proposed to explore the acoustic characteristics of snoring sounds throughout the whole night to classify simple snores and OSAHS patients respectively. A series of acoustic features of snoring sounds of are selected based on the Fisher ratio and learned by GMM. Leave-one-subject-out cross validation experiment based on 30 subjects is conducted to validation the proposed model. There are 6 simple snorers (4 male and 2 female) and 24 OSAHS patients (15 male and 9 female) investigated in this work. Results indicates that snoring sounds of simple snorers and OSAHS patients have different distribution characteristics.Main results.The proposed model achieves average accuracy and precision with values of 90.0% and 95.7% using selected features with a dimension of 100 respectively. The average prediction time of the proposed model is 0.134 ± 0.005 s.Significance.The promising results demonstrate the effectiveness and low computational cost of diagnosing OSAHS patients using snoring sounds at home.

Fluorescent Nanoparticle-RNAi-Mediated Silencing of Sterol Carrier Protein-2 Gene Expression Suppresses the Growth, Development, and Reproduction of Helicoverpa armigera.

Helicoverpa armigera is a polyphagous destructive lepidopteran pest with strong Bacillus thuringiensis (Bt) resistance. Cholesterol, a vital component for insect growth, can only be obtained from food, and its transfer and metabolism are regulated by sterol carrier protein-2 (SCP-2). This study examined whether H. armigera SCP-2 (HaSCP-2) gene expression, involved in cholesterol absorption, can be silenced by nanocarrier fluorescent nanoparticle-RNA interference (FNP-RNAi) by larval feeding and whether the silencing affected H. armigera development. Fluorescence microscopy showed that nanoparticle-siRNA was distributed in Ha cells and the larval midgut. FNP-HaSCP-2 siRNA suppressed HaSCP-2 expression by 52.5% in H.armigera Ha cells. FNP can effectively help deliver siRNA into cells, protect siRNA, and is not affected by serum. FNP-siRNA in vivo biological assays showed that HaSCP-2 transcript levels were inhibited by 70.19%, 68.16%, and 67.66% in 3rd, 4th, and 5th instar larvae, leading to a decrease in the cholesterol level in the larval and prepupal fatbodies. The pupation rate and adult emergence were reduced to 26.0% and 56.52%, respectively. This study demonstrated that FNP could deliver siRNA to cells and improve siRNA knockdown efficiency. HaSCP-2 knockdown by FNP-siRNA in vivo hindered H. armigera growth and development. FNP could enhance RNAi efficiency to achieve pest control by SCP-2-targeted FNP-RNAi.

Laparoscopic central hepatectomy using a parenchymal-first approach: how we do it.

BACKGROUND: Laparoscopic central hepatectomy (LCH) is a difficult and challenging procedure. This study aimed to describe our experience with LCH using a parenchymal-first approach. METHODS: Between July 2017 and June 2021, 19 consecutive patients underwent LCH using a parenchymal-first approach at our institution. Herein, the details of this procedural strategy are described, and the demographic and clinical data of the included patients were retrospectively analyzed. RESULTS: There were 1 female and 18 male patients, all with hepatocellular carcinoma without major vascular invasion. The mean age was 57 ± 10 years. No patients underwent conversion to open surgery, and no blood transfusions were needed intraoperatively. The average operative duration and the average Pringle maneuver duration were 223 ± 65 min and 58 ± 11 min. respectively. The median blood loss was 200 ml (range: 100-800 ml). Postoperative morbidities occurred in 3 patients (15.8%), including 2 cases of bile leakage and 1 case of acquired pulmonary infection; there were no postoperative complications happened such as bleeding, hepatic failure, or mortality. The average postoperative hospital stay was 10 ± 3 days. CONCLUSION: The optimized procedure of LCH using a parenchymal-first approach is not only feasible but also expected to provide an advantage in laparoscopic anatomical hepatectomy.

An OSAHS evaluation method based on multi-features acoustic analysis of snoring sounds.

Snoring is the most direct symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) and implies a lot of information about OSAHS symptoms. This paper aimed to identify OSAHS patients by analyzing acoustic features derived from overnight snoring sounds. Mel-frequency cepstral coefficients, 800 Hz power ratio, spectral entropy and other 10 acoustic features were extracted from snores, and Top-6 features were selected from the extracted 10 acoustic features by a feature selection algorithm based on random forest, then 5 kinds of machine learning models were applied to validate the effectiveness of Top-6 features on identifying OSAHS patients. The results showed that when the classification performance and computing efficiency were taken into account, the combination of logistic regression model and Top-6 features performed best and could successfully distinguish OSAHS patients from simple snorers. The proposed method provides a higher accuracy for evaluating OSAHS with lower computational complexity. The method has great potential prospect for the development of a portable sleep snore monitoring device.

Identification and Functional Analysis of Apoptotic Protease Activating Factor-1 (Apaf-1) from Spodoptera litura.

Apoptotic protease activating factor-1 (Apaf-1) is an adaptor molecule, essential for activating initiator caspase and downstream effector caspases, which directly cause apoptosis. In fruit flies, nematodes, and mammals, Apaf-1 has been extensively studied. However, the structure and function of Apaf-1 in Lepidoptera remain unclear. This study identified a novel Apaf-1 from Spodoptera litura, named Sl-Apaf-1. Sl-Apaf-1 contains three domains: a CARD domain, as well as NOD and WD motifs, and is very similar to mammalian Apaf-1. Interference of Sl-apaf-1 expression in SL-1 cells blocked apoptosis induced by actinomycin D. Overexpression of Sl-apaf-1 significantly enhances apoptosis induced by actinomycin D in Sf9/SL-1/U2OS cells, suggesting that the function of Sl-Apaf-1 is evolutionarily conserved. Furthermore, Sl-Apaf-1 could interact with Sl-caspase-5 (a homologue of mammalian caspase-9) and yielded a binding affinity of 1.37 × 106 M-1 according isothermal titration calorimetry assay. Initiator caspase (procaspase-5) of S. litura could be activated by Sl-Apaf-1 (without WD motif) in vitro, and the activated Sl-caspase-5 could cleave Sl-procaspase-1 (a homologue of caspase-3 in mammals), which directly caused apoptosis. This study demonstrates the key role of Sl-Apaf-1 in the apoptosis pathway, suggesting that the apoptosis pathway in Lepidopteran insects and mammals is conserved.

Experimental Study on Chloride Ion Diffusion in Concrete under Uniaxial and Biaxial Sustained Stress.

The existence of axial and lateral compressive stress affect the diffusion of chloride ions in concrete will lead to the performance degradation of concrete structure. This paper experimentally studied the chloride diffusivity properties of uniaxial and biaxial sustained compressive stress under one-dimensional chloride solution erosion. The influence of different sustained compressive stress states on chloride ion diffusivity is evaluated by testing chloride concentration in concrete. The experiment results show that the existence of sustained compressive stress does not always inhibit the diffusion of chloride ions in concrete, and the numerical value of sustained compressive stress level can affect the diffusion law of chloride ions in concrete. It is found that the chloride concentration decreases most when the lateral compressive stress level is close to 0.15 times the compressive strength of concrete. In addition, the sustained compressive stress has a significant effect on chloride ion diffusion of concrete with high water/cement ratio. Then, the chloride diffusion coefficient model under uniaxial and biaxial sustained compressive stress is established based on the apparent chloride diffusion coefficient. Finally, the results demonstrate that the chloride diffusion coefficient model is reasonable and feasible by comparing the experimental data in the opening literature with the calculated values from the developed model.

Automatic snoring sounds detection from sleep sounds based on deep learning.

Snoring is a typical characteristic of obstructive sleep apnea hypopnea syndrome (OSAHS) and can be used for its diagnosis. The purpose of this paper is to develop an automatic snoring detection algorithm for classifying snore and non-snore sound segments, which have been segmented from a whole-night sleep sound signal using a spectral entropy method, based on convolutional neural network (CNN) descriptors extracted from audio maps. For each sound segment, the time-domain waveform, spectrum, spectrogram, Mel-spectrogram and CQT-spectrogram are calculated. Two classifiers are applied to classify sound segments into either snore or non-snore classes. The first classifier is referred to as CNNs-DNNs and combines CNNs and deep neural networks (DNNs), and the second classifier is referred to as CNNs-LSTMs-DNNs and consists of CNNs, Long and Short memory networks (LSTMs) and DNNs. The results show that the Mel-spectrogram can better reflect the differences between snore and non-snore sound segments for the five maps extracted in this study. Furthermore, the deep spectrum features extracted from CNNs-LSTMs-DNNs using Mel-spectrogram are well suited to this task. The results indicate that the method developed in this study could be used for a portable sleep monitoring device.

Laparoscopic Anatomical Segment VII Resection for Hepatocellular Carcinoma Using the Glissonian Approach with Indocyanine Green Dye Fluorescence.

BACKGROUND: Many studies confirm that anatomical resection was associated with favorable oncologic outcomes for patients with HCC who had preserved as much of the remnant liver tissue as possible.1,2 In recent years, laparoscopic liver resection has been widely extended from minor resection to complex hepatectomy,3 However, surgery on tumors located in the posterosuperior segment remains a demanding procedure regardless of the extent of resection.4 Laparoscopic anatomical segment VII resection has one of the highest difficulty scores based on the tumor location due to poor accessibility, hard to exposure, and difficulty in obtaining sufficient surgical margins.5,6 Here, we report a totally laparoscopic anatomical VII resection using the Glissonian approach with indocyanine green dye fluorescence. METHODS: A 74-year-old man with a body mass index of 31.9 kg/m2 suffered from HBV-related cirrhosis was admitted to our institution. The preoperative Gd-EOB-DTPA MRI showed a 2.7-cm HCC located in segment VIII. The preoperative AFP is 3431 ng/ml. A true anatomical segmentectomy was performed by using selective occlusion of segment VII Glissonian pedicle, which was identified from the liver hilum. Indocyanine green (ICG) dye demarcation was used as a guidance during parenchymal transection. RESULTS: The operative time was 270 min with an estimated blood loss of 200 mL. The postoperative course was uneventful. Drainage tube was pulled out on the fourth day. The pathology confirmed the diagnosis of hepatocellular carcinoma and the surgical margin was negative. The patient was discharged on the 8th day after operation. CONCLUSIONS: Totally laparoscopic anatomical segment VII resection is a technically challenging operation. Advanced laparoscopic skills are necessary to complete such a difficult procedure safely. Glissonian approach and ICG fluorescence imaging guide parenchyma resection could be help.

Deep CovDenseSNN: A hierarchical event-driven dynamic framework with spiking neurons in noisy environment.

Neurons in the brain use an event signal, termed spike, encode temporal information for neural computation. Spiking neural networks (SNNs) take this advantage to serve as biological relevant models. However, the effective encoding of sensory information and also its integration with downstream neurons of SNNs are limited by the current shallow structures and learning algorithms. To tackle this limitation, this paper proposes a novel hybrid framework combining the feature learning ability of continuous-valued convolutional neural networks (CNNs) and SNNs, named deep CovDenseSNN, such that SNNs can make use of feature extraction ability of CNNs during the encoding stage, but still process features with unsupervised learning rule of spiking neurons. We evaluate them on MNIST and its variations to show that our model can extract and transmit more important information than existing models, especially for anti-noise ability in the noisy environment. The proposed architecture provides efficient ways to perform feature representation and recognition in a consistent temporal learning framework, which is easily adapted to neuromorphic hardware implementations and bring more biological realism into modern image classification models, with the hope that the proposed framework can inform us how sensory information is transmitted and represented in the brain.

Major hepatectomy for primary hepatolithiasis: a comparative study of laparoscopic versus open treatment.

BACKGROUND: Due to higher technical requirements, laparoscopic major hepatectomy (LMH) for primary hepatolithiasis have been limited to a few institutions. This retrospective study was performed to evaluate the therapeutic safety, and perioperative and long-term outcomes of LMH versus open major hepatectomy (OMH) for hepatolithiasis. METHODS: From January 2012 to December 2016, 61 patients with hepatolithiasis who underwent major hepatectomy were enrolled, including 29 LMH and 32 OMH. The perioperative outcomes and postoperative complications, as well as long-term outcomes, including the stone clearance and recurrence rate, were evaluated. RESULTS: There was no difference of surgical procedures between the two groups. The mean operation time was (262 ± 83) min in the LMH group and (214 ± 66) min in the OMH group (p = 0.05). There is no difference of intra-operative bleeding (310 ± 233) ml versus (421 ± 359) ml (p = 0.05). In the LMH group, there were shorter time to postoperative oral intake ((1.1 ± 0.6) days versus (3.1 ± 1.8) days, p = 0.01) and shorter hospital stay [(7.2 ± 2.3) days versus (11.8 ± 5.5) days, p = 0.03] than the open group. The LMH group had comparable stone clearance rate with the OMH group during the initial surgery (82.8% vs. 84.4%, p = 0.86). CONCLUSIONS: LMH could be an effective and safe treatment for selected patients with hepatolithiasis, with an advantage over OMH in the field of less intra-operative blood loss, less intra-operative transfusion, less overall complications, and faster postoperative recovery.

Tramadol attenuates the sensitivity of glioblastoma to temozolomide through the suppression of Cx43­mediated gap junction intercellular communication.

Analgesics and antineoplastic drugs are often used concurrently for cancer patients. Our previous study reported that gap junctions composed of connexin32 (Cx32) was implicated in the effect of analgesics on cisplatin cytotoxicity. However, the effect of analgesic on the most widely expressed connexin (Cx), connexin43 (Cx43), and whether such effect mediates the influence on chemotherapeutic efficiency remain unknown. By manipulation of Cx43 expression or gap junction function, we found that there were gap junction-dependent and independent effect of Cx43 on temozolomide (TMZ) sensitivity in U87 glioblastoma cells. Studies on survival and apoptosis showed widely used analgesic tramadol significantly reduced TMZ-induced cytotoxicity in control and negative control cells but not shCx43-transfected cells. Proliferation assay demonstrated tramadol suppressed TMZ-induced cytotoxicity only on high density (with gap junction formation) but not on low density (without gap junction formation). Tramadol inhibited dye-coupling through gap junctions between U87 cells. Tramadol treatment for 72 h did not alter Cx43 expression, but decreased Cx43 phosphorylation accompanied with reduced p-ERK and p-JNK. Our results indicated that long-term treatment with tramadol reduced TMZ cytotoxicity in U87 cells by suppressing Cx43-composed gap junctions, suggesting identification and usage of antinociceptive drugs which do not downregulate connexin activity should have beneficial therapeutic consequences.

FOXA transcriptional factor modulates insect susceptibility to Bacillus thuringiensis Cry1Ac toxin by regulating the expression of toxin-receptor ABCC2 and ABCC3 genes.

Cry toxins produced by Bacillus thuringiensis (Bt) are insecticidal proteins widely used in insect control. Recently, it was shown that ATP-binding cassette transporter proteins (ABC) such as ABCC2, ABCC3, ABCG1 and ABCA2 are implicated in the insecticidal action of Cry toxins as putative receptors. However, the transcriptional regulators involved in the expression of ABC transporter genes remain unknown. Sequence analysis of promoter regions of ABCC2 gene from Helicoverpa armigera and ABCC3 gene from Spodoptera litura Sl-HP cultured cells, revealed the potential participation of Forkhead box protein A (FOXA), a transcription factor that regulates the expression of genes through remodeling chromatin. To determine if FOXA was involved in regulating expression of ABCC2 and ABCC3 genes, the expression of FOXA, ABCC2 and ABCC3 was compared in Sl-HP cells that are sensitive to Cry1Ac toxin with those in S. frugiperda Sf9 cells that are not sensitive to the toxin. Expression levels of those genes were significantly higher in Sl-HP than in Sf9 cells. Transient expression of FOXA in Sf9 cells activated ABCC2 and ABCC3 transcription, which directly correlated with enhanced Cry1Ac-susceptibility in these cells. Silencing of FOXA gene expression by RNAi in H. armigera larvae resulted in a decreased expression of ABCC2 and ABCC3 without affecting expression of other Cry toxin receptor genes such as alkaline phosphatase, aminopeptidase or cadherin. Silencing of FOXA gene expression also resulted in a Cry1Ac-tolerant phenotype since lower mortality and higher pupation rate were observed in diet containing Cry1Ac protoxin in comparison with the control group. These results demonstrate that FOXA up-regulates expression of the Cry1Ac-toxin receptor ABCC2 and ABCC3 genes, and that lower FOXA expression correlates with tolerance to Cry toxin in cell lines and in lepidopteran larvae.