Effect of Brønsted Acids on the Activation of Mixed Anhydride/Mixed Carbonic Anhydride and C-Terminal-Free N-Methylated Peptide Synthesis in a Micro-Flow Reactor.

Amidations employing mixed (carbonic) anhydrides have long been favoured in peptide synthesis because of their cost-effectiveness and less waste generation. Despite their long history, no study has compared the effects of additives on the activation of mixed anhydrides and carbonic anhydrides. In this study, we investigated the amidation of mixed (carbonic) anhydride in the presence of a base and/or Brønsted acids. The use of NMI⋅HCl significantly improved the conversion of the mixed carbonic anhydride, while expediting nucleophilic attacks on the desired carbonyl group. In contrast, in the case of mixed anhydrides, neither the conversion nor the desired nucleophilic attack improved significantly. We developed a C-terminus-free N-methylated peptide synthesis method using mixed carbonic anhydrides in a micro-flow reactor. Fourteen N-alkylated peptides were synthesized in moderate to high yields (55-99 %) without severe racemization (<1 %). Additionally, a significant enhancement in the amidation between mixed carbonic anhydrides and bis-TMS-protected N-methyl amino acids with the inclusion of NMI⋅HCl was observed for the first time. In addition, we observed unexpected C-terminal epimerization of the C-terminus-free N-methyl peptides.

Peptide Cyclization by the Use of Acylammonium Species.

Although cyclic peptides have become increasingly important as drugs, the most conventional peptide cyclization method using moderately active coupling agents suffers from a lot of waste and high cost as well as long reaction times and burdensome purification. Herein, we report an unconventional approach to peptide cyclization that uses acylammonium species generated from inexpensive and less wasteful Me2 NBn and ClCO2 i-Pr. Using this approach, we observed the desired rapid activation of the C-terminus of cyclization precursors by an acylammonium ion for rapid and epimerization/dimerization-free cyclization of synthetically challenging peptides, including a difficult cyclization involving N-methyl amide bond formation. The ease of purification, productivities, and reaction mass efficiencies of our approach were significantly superior to those in previous reports. We synthesized a previously reported versicotide D analogue, and our data indicated that its assigned stereostructure should be revised.

Acupuncture for de Quervain's tenosynovitis: A randomized controlled trial.

BACKGROUND: Acupuncture has been an alternative approach for de Quervain's tenosynovitis (DQt), but trial evidence is still lacking. PURPOSE: This study aimed to assess the efficacy of acupuncture in patients with DQt. STUDY DESIGN: A randomized controlled trial. METHODS: A total of 68 subjects with DQt were recruited from outpatients of Department of Orthopaedics and Traumatology, and Chinese medicine clinics, The University of Hong Kong, and were randomized into the acupuncture group (n = 34) and the waitlist group (n = 34). Subjects in the acupuncture group received 5 acupuncture sessions over 2 weeks, followed by a 10-week follow-up. The waitlist control group received assessments only in the first 6 weeks of the waiting period and received the same acupuncture treatment and follow-up as the treatment group in the next 12 weeks. The primary outcome was the general pain intensity using the Visual Analogue Scale (VAS) at the end of treatment (week 2). Secondary outcomes were grip and pinch strengths of affected hands, the quick Disabilities of the Arm, Shoulder and Hand Score (Q-DASH), and the World Health Organization Quality of Life-brief Questionnaire (WHOQOL-BREF) at weeks 2 and 6. RESULTS: From baseline to 2 weeks, the mean VAS score decreased by 19.5 points in the acupuncture group and by 3.4 points in the waitlist group. The difference for acupuncture vs waitlist control was -16.2 points (95% CI, -26.7 to -5.6, p = 0.003). Acupuncture effects sustained for 10 weeks (mean difference compared with baseline, -30.6; 95% CI, -39.6 to -21.7). Secondary outcomes showed that acupuncture reduced pain intensity, improved grip and pinch strength of affected hands, and Q-DASH scores, but not the scores of WHOQOL-BREF in patients. No serious adverse event occurred during the study period. CONCLUSIONS: Our findings support that 2-week of acupuncture is safe and effective in the reduction of pain intensity, and improvement of strengths and disabilities of hand in DQt patients. Acupuncture also has long-term effects on DQt. TRIAL REGISTRATION: This study was registered at clinicaltrials.gov (NCT03472443).

Synthesis and structure-activity relationship of teixobactin analogues via convergent Ser ligation.

Convergent Ser/Thr ligation has been used to prepare a series of teixobactin analogues (28 in total) to establish a structure-activity relationship of teixobactin. anti-bacterial evaluations of these synthetic analogues have revealed the critical amino acid residues and the sites tolerable of modifications. These studies will shed lights on the further development of teixobactin analogues with improved antibacterial activities.

Entecavir treatment reduces hepatic events and deaths in chronic hepatitis B patients with liver cirrhosis.

UNLABELLED: Entecavir is a potent antiviral agent with high genetic barrier to resistance, hence it is currently recommended as first-line antiviral therapy for chronic hepatitis B (CHB). The aim of this study was to investigate the efficacy of entecavir on clinical outcomes and deaths. It was a retrospective-prospective cohort study based on two cohorts of patients. The entecavir cohort included consecutive CHB patients who had received entecavir 0.5 mg/day for at least 12 months. The historical control cohort included untreated patients recruited since 1997 who underwent routine clinical care. The primary outcome was the 5-year cumulative probability of hepatic events, defined as any cirrhotic complications, hepatocellular carcinoma (HCC), and/or liver-related mortality. A total of 1,446 entecavir-treated patients (72% men; age, 51 ± 12 years; follow-up, 36 ± 13 months) and 424 treatment-naïve patients (65% men; age, 41 ± 13 years; follow-up, 114 ± 31 months) were studied. Overall, there was no difference in hepatic events between the entecavir and control cohorts. Among patients with liver cirrhosis (482 entecavir-treated, 69 treatment-naïve), entecavir-treated patients had reduced risks of all clinical outcomes when compared with treatment-naïve patients with cirrhosis after adjusted for model for end-stage liver disease score: hepatic events (hazard ratio [HR], 0.51; 95% confidence interval [CI], 0.34-0.78; P = 0.002), HCC (HR, 0.55; 95% CI, 0.31-0.99; P = 0.049), liver-related mortality (HR, 0.26; 95% CI, 0.13-0.55; P < 0.001), and all-cause mortality (HR, 0.34; 95% CI, 0.18-0.62; P < 0.001). Entecavir-treated patients with cirrhosis who failed to achieve undetectable hepatitis B virus DNA (105/482 [22%]) had comparable risk of hepatic events as the untreated patients. CONCLUSION: Entecavir therapy reduces the risks of hepatic events, HCC, liver-related and all-cause mortality of CHB patients with liver cirrhosis in 5 years, particularly among those who had maintained viral suppression.

Accuracy of risk scores for patients with chronic hepatitis B receiving entecavir treatment.

BACKGROUND & AIMS: Little is known about the validity of hepatocellular carcinoma (HCC) risk scores derived from treatment-naïve patients with chronic hepatitis B for patients treated with entecavir. METHODS: We performed a retrospective-prospective cohort study of 1531 patients with chronic hepatitis B (age, 51 ± 12 years; 1099 male; 332 with clinical cirrhosis) who were treated with entecavir 0.5 mg daily for at least 12 months at Prince of Wales Hospital in Hong Kong from December 2005 to August 2012. The patients were assessed once every 3 to 6 months for symptoms, drug history, and adherence; blood samples were collected for biochemical analyses. We validated 3 HCC risk scores (CU-HCC, GAG-HCC, and REACH-B scores) based on data collected when patients began treatment with entecavir and 2 years later. RESULTS: After 42 ± 13 months of follow-up, 47 patients (2.9%) developed HCC. The 5-year cumulative incidence of HCC was 4.3% (95% confidence interval [CI], 3.6%-5.0%). Older age, presence of cirrhosis, and virologic remission after 24 months or more of therapy were independently associated with HCC in the entire cohort; advanced age and hypoalbuminemia were associated with HCC in patients without cirrhosis. The area under the receiver operating characteristic curves (AUCs) for baseline CU-HCC, GAG-HCC, and REACH-B scores for HCC were 0.80 (95% CI, 0.75-0.86), 0.76 (95% CI, 0.70-0.82), and 0.71 (95% CI, 0.62-0.81), respectively; the time-dependent AUCs 1 to 4 years after patients started treatment were comparable to those at baseline. The cutoff value of the baseline CU-HCC score identified patients who would develop HCC with 93.6% sensitivity and 47.8% specificity, the baseline GAG-HCC score with 55.3% sensitivity and 78.9% specificity, and the baseline REACH-B score with 95.2% sensitivity and 16.5% specificity. Compared with patients with CU-HCC scores <5 at baseline, those with CU-HCC scores that either decreased from ≥5 to <5 or remained ≥5 had a higher risk of HCC (5-year cumulative incidences, 0% vs 3.9% and 7.3%; P = .002 and P < .001, respectively). CONCLUSIONS: The CU-HCC, GAG-HCC, and REACH-B HCC risk scores accurately predict which patients with chronic hepatitis B treated with entecavir will develop HCC.

A low-order model of biological neural networks.

A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.

Erratum to: Functional model of biological neural networks.

[This corrects the article DOI: 10.1007/s11571-010-9110-4.].

Functional model of biological neural networks.

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

Adaptive capability of recurrent neural networks with fixed weights for series-parallel system identification.

By a fundamental neural filtering theorem, a recurrent neural network with fixed weights is known to be capable of adapting to an uncertain environment. This letter reports some mathematical results on the performance of such adaptation for series-parallel identification of a dynamical system as compared with the performance of the best series-parallel identifier possible under the assumption that the precise value of the uncertain environmental process is given. In short, if an uncertain environmental process is observable (not necessarily constant) from the output of a dynamical system or constant (not necessarily observable), then a recurrent neural network exists as a series-parallel identifier of the dynamical system whose output approaches the output of an optimal series-parallel identifier using the environmental process as an additional input.

Predictors of fibrosis in Asian patients with non-alcoholic steatohepatitis.

BACKGROUND AND AIM: Non-alcoholic steatohepatitis (NASH) is increasingly recognized as an important cause of chronic liver disease. However, data on Asians with NASH is lacking in the literature. The aim of the present study was to describe the clinical, biochemical and histological characteristics of NASH in Asians and to determine the predictors for septal fibrosis. METHOD: Sixty consecutive patients aged over 18 years with elevated serum alanine transferase, sonographic evidence of steatosis, and consent for liver biopsy were included. Patients with chronic hepatitis B or C, alcoholic, autoimmune, genetic, or drug-induced liver disease were excluded. Clinical, biochemical and histological variables were tested for association with septal liver fibrosis (F2/3). RESULTS: Median age of the cohort was 45.5 years (range 21-75 years) and 63% were male. Ninety percent of patients were obese (body mass index [BMI]>or= 25), 70% had hypertriglyceridemia, 68% had hypercholesterolemia, 58% had metabolic syndrome, 53% had hypertension, 47% had diabetes mellitus (DM), and 18% had obstructive sleep apnea. Sixty-eight percent had gamma-glutamyl transferase (GGT) >or= 2 x upper limit of normal (ULN), 55% had alanine aminotransferase (ALT) >or= 2 x ULN, and 23% had aspartate aminotransferase (AST) >or= 2 x ULN. Of the 40 non-diabetic patients undergoing oral glucose tolerance testing, 45% had normal tests, 30% had impaired glucose tolerance, 23% DM, and 2% impaired fasting glucose. Eighteen patients (30%) had septal fibrosis (F2/3), but none had cirrhosis. Necroinflammatory grade >or= 2 (odds ratio [OR] 13), AST >or= 2 x ULN (OR 5.3) and DM (OR 5) were significantly and independently correlated with septal fibrosis. CONCLUSION: Septal fibrosis is common in Asians with NASH. Necroinflammatory grade >or= 2, AST >or= 2 x ULN and DM are independent predictors for septal fibrosis.

Gene expression profiles of cold-stored and fresh pollen to investigate pollen germination and growth.

In lily (Lilium longiflorum cv. Avita) pollen cold-stored (-20 degrees C) for 2 months, typical in vitro germination/growth was delayed by about 1 h compared with fresh pollen. We hypothesized that some proteins and mRNAs stored in mature pollen were degraded during storage periods and that re-synthesis of them was essential to resume normal germination and growth. Cold-stored and fresh pollen grains were used to investigate the regulatory mechanism of pollen germination and tube growth in terms of both total protein profile and gene expression. Total protein profiles of cold-stored pollen differed qualitatively and quantitatively from fresh pollen. Actinomycin D significantly inhibited both germination and tube growth of cold-stored pollen and later tube growth of fresh pollen but had no effect on fresh pollen germination and early tube growth. Suppression subtractive hybridization screening revealed 99 cDNAs enriched in fresh mature pollen, and 22 were selected for further characterization. Most of these 22 cDNAs gradually disappeared during cold storage, but full recovery was achieved by incubating the cold-stored pollen in culture medium for 2 h. Because of different sensitivities to cold storage and actinomycin D, the transcripts were divided into three groups according to their possible roles in pollen germination and tube growth. Several cDNAs encoding novel proteins showed pollen-specific expression patterns and may participate in drought tolerance (an Na+/H+ antiporter), endomembrane trafficking (DnaJ), division of the generative cell (Sgt1), pollen wall precursor uptake from stylar exudate (an Na+/myoinositol symporter) and chemotropism of the pollen tube (peptide transporter) during pollination.

Synthetic approach to optimal filtering.

As opposed to the analytic approach used in the modern theory of optimal filtering, a synthetic approach is presented. The signal/sensor data, which are generated by either computer simulation or actual experiments, are synthesized into a filter by training a recurrent multilayer perceptron (RMLP) with at least one hidden layer of fully or partially interconnected neurons and with or without output feedbacks. The RMLP, after adequate training, is a recursive filter optimal for the given structure, with the lagged feedbacks carrying the optimal conditional statistics at each time point. Above all, it converges to the minimum variance filter as the number of hidden neurons increases. We call such an RMLP a neural filter. Simulation results show that the neural filters with only a few hidden neurons consistently outperform the extended Kalman filter and even the iterated extended Kalman filter for the simple nonlinear signal/sensor systems considered.