Permeation Mechanism of Potassium Ions through the Large Conductance Ca2+-Activated Potassium Channel

The permeation of the potassium ion (K+) through the selectivity filter (SF) of the large conductance Ca2+-activated potassium (Slo1) channel remains an interesting question to study. Although the mode of K+ entering and leaving the SF has been revealed, the mechanism of K+ passing through the SF is still not clear. In the present study, the pattern of K+ permeation through the SF is investigated by chemical computation and data mining based on the molecular structure of Slo1 from Aplysia californica. Both bond configurations and the free energy of K+s inside the SF was studied using Discovery Studio software. The results suggested that, to accommodate increasing energy levels and to tolerate more K+s, 4-fold symmetric subunits of SF can only move at one direction that is perpendicular to the center axis. In addition, two configurations of chemical bonds between K+s and the SF are usually employed including the chelate configuration under low free energy and the complex configuration under high free energy conditions. Moreover, three patterns of bond configurations for multiple K+s within the SF are used to accommodate the energetic changes of the SF, and each pattern is composed of one or two subconformations. These findings likely resulted from the evolutionary optimization of the protein function of Slo1. The specific conductance and the voltage-gating of the Slo1 channel can be reinterpreted with the permeation mechanism of K+s found in the current study. The permeation mechanism of K+s through the SF can be used to understand the interaction between various toxins and the Slo1 channel, and can be employed to develop new drugs targeting relevant ion channels

Utilization and spending trends for antiretroviral medications in the U.S. Medicaid program from 1991 to 2005

<p>Abstract</p> <p>Background</p> <p>HIV/AIDS incidence and mortality rates have decreased in the U.S. since 1996. Accompanying the longer life spans of those diagnosed with the disease, however, is a tremendous rise in expenditures on medication. The objective of this study is to describe the trends in utilization of, spending on, and market shares of antiretroviral medications in the U.S. Medicaid Program. Antiretroviral drugs include nucleoside reverse transcriptase inhibitors (NRTIs), protease inhibitors (PIs), nonnucleoside reverse transcriptase inhibitors (NNRTIs), and fusion inhibitors (FIs).</p> <p>Methods</p> <p>Utilization and payment data from 1991 to 2005 are provided by the Centers for Medicare & Medicaid Services. Descriptive summary analyses were used to assess quarterly prescription numbers and amounts of payment.</p> <p>Results</p> <p>The total number of prescriptions for antiretrovirals increased from 168,914 in 1991 to 2.0 million in 1998, and 3.0 million in 2005, a 16.7-fold increase over 15 years. The number of prescriptions for NRTIs reached 1.6 million in 2005. Prescriptions for PIs increased from 114 in 1995 to 932,176 in 2005, while the number of prescriptions for NNRTIs increased from 1,339 in 1996 to 401,272 in 2005. The total payment for antiretroviral drugs in the U.S. Medicaid Program increased from US$30.6 million in 1991 to US$ 1.6 billion in 2005, a 49.8-fold increase. In 2005, NRTIs as a class had the highest payment market share. These drugs alone accounted for US$787.9 million in Medicaid spending (50.8 percent of spending on antiretrovirals). Payment per prescription for each drug, with the exception of Agenerase^®, increased, at least somewhat, over time. The relatively expensive drugs in 2005 included Trizivir^®($1040) and Combivir^®($640), as well as Reyataz^®($750), Lexiva^®($700), Sustiva^®($420), Viramune^®($370), and Fuzeon^®($1914).</p> <p>Conclusion</p> <p>The tremendous growth in antiretroviral spending is due primarily to rising utilization, secondarily to the entry of newer, more expensive antiretrovirals, and, finally, in part to rising per-prescription cost of existing medications.</p

Transgenic soybean production of bioactive human epidermal growth factor (EGF)

Necrotizing enterocolitis (NEC) is a devastating condition of premature infants that results from the gut microbiome invading immature intestinal tissues. This results in a life-threatening disease that is frequently treated with the surgical removal of diseased and dead tissues. Epidermal growth factor (EGF), typically found in bodily fluids, such as amniotic fluid, salvia and mother's breast milk, is an intestinotrophic growth factor and may reduce the onset of NEC in premature infants. We have produced human EGF in soybean seeds to levels biologically relevant and demonstrated its comparable activity to commercially available EGF. Transgenic soybean seeds expressing a seed-specific codon optimized gene encoding of the human EGF protein with an added ER signal tag at the N' terminal were produced. Seven independent lines were grown to homozygous and found to accumulate a range of 6.7 +/- 3.1 to 129.0 +/- 36.7 μg EGF/g of dry soybean seed. Proteomic and immunoblot analysis indicates that the inserted EGF is the same as the human EGF protein. Phosphorylation and immunohistochemical assays on the EGF receptor in HeLa cells indicate the EGF protein produced in soybean seed is bioactive and comparable to commercially available human EGF. This work demonstrates the feasibility of using soybean seeds as a biofactory to produce therapeutic agents in a soymilk delivery platform

Safety and efficacy of iodine-125 seed strand for intraluminal brachytherapy on ureteral carcinoma

ObjectiveOur aim is to evaluate the safety and efficacy of iodine-125 seed strand for intraluminal brachytherapy on ureteral carcinoma.MethodsFrom November 2014 to November 2021, 22 patients with ureteral cancer not suitable for surgical resection were enrolled. Iodine-125 seed strand was inserted under c-arm CT and fluoroscopic guidance. The technical success rate, complications, disease control rate, and survival time were evaluated. Hydronephrosis Girignon grade and ureteral cancer sizes before and after treatment were compared.ResultsA total of 46 seed strands were successfully inserted and replaced, with a technical success rate of 100% and median procedure time of 62 min. No procedure-related death, ureteral perforation, infection, or severe bleeding occurred. Minor complications were observed in eight (36.4%) patients, and migration of seed strand was the most common complication. Six months after seed strand brachytherapy, one complete response, three partial responses, and five stable diseases were evaluated, and the disease control rate was 64.3%. The Girignon grade of hydronephrosis was significantly improved 1 to 3 months after seed strand insertion. Disease control rates were 94.4, 62.5, and 64.3% at 1-, 3-, and 6-month follow-up. Twenty patients were successfully followed up, with a mean follow-up of 18.0 ± 14.5 months. The median overall survival and progress-free survival were 24.7 and 13.0 months, respectively.ConclusionIodine-125 seed strand is safe and effective for intraluminal brachytherapy and can be used as an alternative to patients with ureteral carcinoma who are not suitable for surgical resection or systemic combined therapy

The RALF1–FERONIA Complex Phosphorylates eIF4E1 to Promote Protein Synthesis and Polar Root Hair Growth

The molecular links between extracellular signals and the regulation of localized protein synthesis in plant cells are poorly understood. Here, we show that in Arabidopsis thaliana, the extracellular peptide RALF1 and its receptor, the FERONIA receptor kinase, promote root hair (RH) tip growth by modulating protein synthesis. We found that RALF1 promotes FERONIA-mediated phosphorylation of eIF4E1, a eukaryotic translation initiation factor that plays a crucial role in the control of mRNA translation rate. Phosphorylated eIF4E1 increases mRNA affinity and modulates mRNA translation and, thus, protein synthesis. The mRNAs targeted by the RALF1–FERONIA–eIF4E1 module include ROP2 and RSL4, which are important regulators of RH cell polarity and growth. RALF1 and FERONIA are expressed in a polar manner in RHs, which facilitate eIF4E1 polar localization and thus may control local ROP2 translation. Moreover, we demonstrated that high-level accumulation of RSL4 exerts negative-feedback regulation of RALF1 expression by directly binding the RALF1 gene promoter, determining the final RH size. Our study reveals that the link between RALF1–FERONIA signaling and protein synthesis constitutes a novel component regulating cell expansion in these polar growing cells.Fil: Zhu, Sirui. Hunan University; ChinaFil: Estevez, Jose Manuel. Universidad Andrés Bello; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Liao, Hongdong. Hunan University; ChinaFil: Zhu, Yonghua. Hunan University; ChinaFil: Yang, Tao. Central South University of Forestry and Technology; ChinaFil: Li, Chiyu. Hunan University; ChinaFil: Wang, Yichuan. Southern University of Science and Technology; ChinaFil: Li, Lan. Hunan University; ChinaFil: Liu, Xuanming. Hunan University; ChinaFil: Martinez Pacheco, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Guo, Hongwei. Southern University of Science and Technology; ChinaFil: Yu, Feng. Hunan University; Chin

Feature-based Transferable Disruption Prediction for future tokamaks using domain adaptation

The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak only using a few discharges based on a domain adaptation algorithm called CORAL. It is the first attempt at applying domain adaptation in the disruption prediction task. In this paper, this disruption prediction approach aligns a few data from the future tokamak (target domain) and a large amount of data from the existing tokamak (source domain) to train a machine learning model in the existing tokamak. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak. To simulate the lack of disruptive data in future tokamak, we only selected 100 non-disruptive discharges and 10 disruptive discharges from EAST as the target domain training data. We have improved CORAL to make it more suitable for the disruption prediction task, called supervised CORAL. Compared to the model trained by mixing data from the two tokamaks, the supervised CORAL model can enhance the disruption prediction performance for future tokamaks (AUC value from 0.764 to 0.890). Through interpretable analysis, we discovered that using the supervised CORAL enables the transformation of data distribution to be more similar to future tokamak. An assessment method for evaluating whether a model has learned a trend of similar features is designed based on SHAP analysis. It demonstrates that the supervised CORAL model exhibits more similarities to the model trained on large data sizes of EAST. FTDP provides a light, interpretable, and few-data-required way by aligning features to predict disruption using small data sizes from the future tokamak.Comment: 15 pages, 9 figure

Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection

The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-driven disruption predictors employ supervised learning techniques, which require labeled training data. The manual labeling of disruption precursors is a tedious and challenging task, as some precursors are difficult to accurately identify, limiting the potential of machine learning models. To address this issue, commonly used labeling methods assume that the precursor onset occurs at a fixed time before the disruption, which may not be consistent for different types of disruptions or even the same type of disruption, due to the different speeds at which plasma instabilities escalate. This leads to mislabeled samples and suboptimal performance of the supervised learning predictor. In this paper, we present a disruption prediction method based on anomaly detection that overcomes the drawbacks of unbalanced positive and negative data samples and inaccurately labeled disruption precursor samples. We demonstrate the effectiveness and reliability of anomaly detection predictors based on different algorithms on J-TEXT and EAST to evaluate the reliability of the precursor onset time inferred by the anomaly detection predictor. The precursor onset times inferred by these predictors reveal that the labeling methods have room for improvement as the onset times of different shots are not necessarily the same. Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.Comment: 21 pages, 11 figure

Heightened immune response to autocitrullinated porphyromonas gingivalis peptidylarginine deiminase: a potential mechanism for breaching immunologic tolerance in rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is characterised by autoimmunity to citrullinated proteins, and there is increasing epidemiologic evidence linking Porphyromonas gingivalis to RA. P gingivalis is apparently unique among periodontal pathogens in possessing a citrullinating enzyme, peptidylarginine deiminase (PPAD) with the potential to generate antigens driving the autoimmune response. Objectives: To examine the immune response to PPAD in patients with RA, individuals with periodontitis (PD) and controls (without arthritis), confirm PPAD autocitrullination and identify the modified arginine residues. Methods: PPAD and an inactivated mutant (C351A) were cloned and expressed and autocitrullination of both examined by immunoblotting and mass spectrometry. ELISAs using PPAD, C351A and another P gingivalis protein arginine gingipain (RgpB) were developed and antibody reactivities examined in patients with RA (n=80), individuals with PD (n=44) and controls (n=82). Results: Recombinant PPAD was a potent citrullinating enzyme. Antibodies to PPAD, but not to Rgp, were elevated in the RA sera (median 122 U/ml) compared with controls (median 70 U/ml; p&#60;0.05) and PD (median 60 U/ml; p&#60;0.01). Specificity of the anti-peptidyl citrullinated PPAD response was confirmed by the reaction of RA sera with multiple epitopes tested with synthetic citrullinated peptides spanning the PPAD molecule. The elevated antibody response to PPAD was abolished in RA sera if the C351A mutant was used on ELISA. Conclusions: The peptidyl citrulline-specific immune response to PPAD supports the hypothesis that, as a bacterial protein, it might break tolerance in RA, and could be a target for therapy