Solid-phase synthesis of 16 potent (selective and nonselective) in vivo antagonists of oxytocin

We describe the synthesis and some pharmacological properties of 16 new in vivo antagonists of oxytocin. These are based on modifications of three peptides: A, B, and C. A is our previously reported potent and selective antagonist of the vasopressor (V1 receptor) responses to arginine-vasopressin (AVP)/weak oxytocin antagonist, [1-(beta-mercapto-beta,beta-pentamethylenepropionic acid), 2-O-methyltyrosine]arginine-vasopressin (d(CH2)5[Tyr(Me)2]AVP. B reported here, the Ile3 analogue of A, is d(CH2)5[Tyr(Me)2]AVT (5 below) and C is our previously reported potent nonselective oxytocin antagonist/AVP V1 antagonist, [1-(beta-mercapto-beta,beta-pentamethylenepropionic acid),2-O- methyltyrosine,8-ornithine]vasotocin (d(CH2)5[Tyr(Me)2]OVT). The following substitutions and deletions, alone or in combination, were employed in A, B, and C: 1-deaminopenicillamine (dP); D-Tyr(Alk)2 (where Alk = Me or Et), D-Phe2; Val4, Thr4; delta 3-Pro7; Lys8, Cit8; desGly9, desGly-NH2(9), Ala-NH2(9); Leu-NH2(9); Arg-NH2(9). The 16 new analogues are (1) d(CH2)5[D-Tyr(Me)2]AVP, (2) d(CH2)5[D-Tyr(Me)2, Val4,delta 3-Pro7]AVP, (3) d(CH2)5[D-Tyr-(Et)2, Val4,Lys8]VP, (4) d(CH2)5[D-Tyr(Et)2,Val4,Cit8]VP, (5) d(CH2)5[Tyr(Me)2]AVT, (6) d(CH2)5[Tyr(Me)2,Lys8]VT, (7) dP[Tyr(Me)2]AVT, (8) dP[Tyr(Me)2,Val4]AVT, (9) d(CH2)5[D-Tyr(Me)2, Val4]AVT, (10) d(CH2)5[D-Phe2,Val4]AVT, (11) d(CH2)5[Tyr(Me)2,Thr4]OVT, (12) d(CH2)5[Tyr(Me)2,Thr4,Ala-NH2(9)]OVT, (13) d(CH2)5[Tyr(Me)2,Thr4,Leu-NH2(9)]OVT, (14) d(CH2)5[Tyr(Me)2,Thr4,Arg-NH2(9)]OVT, (15) desGly-NH2(9),d(CH2)5[Tyr(Me)2,Thr4]OVT, (16) desGly9,d(CH2)5[Tyr(Me)2,Thr4]OVT. 1-4 are analogues of A, 5-10 are analogues of B, and 11-16 are analogues of C. Their protected precursors were synthesized either entirely by the solid-phase method or by a combination of solid-phase and solution methods (1 + 8 or 8 + 1 couplings). All analogues were tested in rats for agonistic and antagonistic activities in oxytocic (in vitro, without and with Mg2+, and in vivo) assays as well as by antidiuretic and vasopressor assays. All analogues exhibit potent oxytocic antagonism in vitro and in vivo. With an in vitro pA2 (in the absence of Mg2+) = 9.12 +/- 0.09, dP[Tyr(Me)2]AVT is (7) one of the most potent in vitro oxytocin antagonists reported to date. Fifteen of these analogues (all but 6) appear as potent or more potent in vivo oxytocin antagonists than C (pA2 = 7.37 +/- 0.17). Analogues 1-9 and 14 are potent AVP V1 antagonists. Their anti-V1 pA2 values range from 7.92 to 8.45. They are thus nonselective oxytocin antagonists.(ABSTRACT TRUNCATED AT 400 WORDS)     

Primary care referrals for lumbar spine radiography: diagnostic yield and clinical guidelines.

BACKGROUND: Primary care requests for radiographs of the lumbar spine have come under increasing scrutiny. Guidelines aiming to reduce unnecessary radiographs by limiting referrals to patients at high risk of serious disease have been widely distributed. Trial evidence suggests that guidelines can reduce radiography referrals. It is not clear whether this reduction has been achieved in routine practice. AIM: This study, using routine data, was conducted to measure trends in pnmary care referrals for lumbar spine radiography at two hospitals between 1994 and 1999. DESIGN OF STUDY: Analysis of primary care requests for lumbar spine radiography from computerised records. SETTING: Addenbrooke's Hospital, Cambridge (1 July 1994 to 30 June 1999), and Ipswich General Hospital (1 July 1995 to 30 June 1999), United Kingdom. METHOD: All primary care requests for lumbar radiography were identified electronically from computerised information systems. A random sample of 2100 radiography reports were classified according to clinical importance. These classifications were used to examine whether the proportion of radiographs demonstrating potentially more serious findings had increased between 1994 and 1999. RESULTS: There was no evidence that primary care referrals for radiography of the lumbar spine had decreased between 1994 and 1999 at either hospital. General practitioners did not progressively refer more high-risk patients for lumbar radiography. Only a small proportion of patients had important radiographic findings that might warrant specialist referral or specific therapy. CONCLUSION: The implementation of diagnostic guidelines offers much to the NHS. However in these two hospitals, the reduction in radiograph utilisation evident in trials was not achieved. Guideline development is a resource intensive process; distribution must be supported by more effective implementation strategies.     

Bilateral hamartoma of tonsils bearing ectopic teeth: a case report

Ectopic teeth presenting in bath the tonsils with hatnartomatous lesion has not been reported earlier. We present here a case of benign hamartoma of both the tonsils bearing 7 teeth in a 13 year old girl and also discuss about unusual Ectopic sites of tooth eruption as well as benign lesions of tonsil.     

Treatment of relapsed chronic lymphocytic leukemia by 72-hour continuous infusion or 1-hour bolus infusion of flavopiridol: results from Cancer and Leukemia Group B study 19805.

PURPOSE: Flavopiridol has in vitro activity in chronic lymphocytic leukemia (CLL) and promotes apoptosis independent of p53 function or prior fludarabine exposure. We sought to determine if flavopiridol has activity in previously treated CLL using two schedules of administration. PATIENTS AND METHODS: Patients with previously treated CLL were enrolled in two sequentially done phase II studies. Patients in the first trial received flavopiridol (50 mg/m(2)/d) as a continuous infusion (CI) for 72 hours every 2 weeks. Patients in the second trial received flavopiridol 50 mg/m(2) as a 1-hour bolus (IVB) daily for 3 days repeated every 3 weeks. Patients received up to 12 (CI cohort) or 8 (IVB cohort) cycles of therapy. RESULTS: Fifteen patients were enrolled in the 72-hour CI phase II trial; 6 (40%) had intermediate-risk (Rai stage I or II) and 9 (60%) had high-risk (Rai stage III and IV) stages. No responses were noted in this group; 27% had stable disease and 73% had progressive disease. Thirty-six patients were enrolled in the second IVB trial, with 13 (36%) having intermediate and 23 (64%) having high-risk disease. Four patients (11%) had partial responses, 19 (53%) had stable disease, and 13 (36%) had progressive disease. The progression-free survival for responders in the IVB trial was 3, 3, 9, and 19 months. The median progression-free survival was 2 months [95% confidence interval (95% CI), 1.8-3.8] for patients in the CI trial and 3 months (95% CI, 2.5-7.4) for the IVB trial. The median overall survival was 27 months (95% CI, 20-42) for the CI trial and 24 months (95% CI, 18-31) for the IVB trial. Toxicity was manageable and included mainly myelosuppression, infections, diarrhea, and fatigue. CONCLUSIONS: Flavopiridol has modest, schedule-dependent clinical activity in relapsed CLL and warrants future investigation utilizing alternative schedules of administration.     

Antitumor activity and antioxidant role of Bauhinia racemosa against Ehrlich ascites carcinoma in Swiss albino mice [corrected]

AIM: To study the antitumor effect and antioxidant role of Bauhinia racemosa. METHODS: Antitumor activity and antioxidant status of methanol extract (50, 100, and 200 mg/kg) of Bauhinia racemosa stem bark was evaluated against Ehrlich ascites carcinoma (EAC) tumor in mice. Acute and short-term toxicity studies were performed initially in order to ascertain the safety of methanol extract of Bauhinia racemosa (MEBR). After 24 h of tumor inoculation, the extract was administered daily for 14 d. After administration of the last dose followed by 18 h fasting, mice were then sacrificed for observation of antitumor activity. The effect of MEBR on the growth of transplantable murine tumor, life span of EAC bearing hosts and simultaneous alterations in the hematological profile and liver biochemical parameters (lipid peroxidation, antioxidant enzymes) were estimated. RESULTS: The MEBR showed decrease in tumor volume, packed cell volume and viable cell count, and increased the nonviable cell count and mean survival time thereby increasing life span of EAC tumor bearing mice. Hematological profile reverted to more or less normal levels in extract treated mice. Treatment with MEBR decreased the levels of lipid peroxidation and increased the levels of glutathione, superoxide dismutase and catalase. CONCLUSION: The methanol extract of Bauhinia racemosa stem bark exhibited antitumor effect by modulating lipid peroxidation and augmenting antioxidant defense system in EAC bearing mice.     

Horizontal transmission of novel H1N1/09 influenza virus in a newborn: Myth or fact?

Horizontal transmission of H1N1/09 virus infection is very common however; transmission through this route has not been reported in newborns. To our knowledge, this is the first case report of newborn who acquired infection of novel H1N1/09 virus horizontally through asymptomatic family members or hospital staff during epidemic period in Kolkata, India. Baby recovered without antiviral therapy but received antibiotic for bacterial co-infection.     

Competitive bidding for health insurance contracts: lessons from the online HMO auctions

Healthcare is an important social and economic component of modern society, and the effective use of information technology in this industry is critical to its success. As health insurance premiums continue to rise, competitive bidding may be useful in generating stronger price competition and lower premium costs for employers and possibly, government agencies. In this paper, we assess an endeavor by several Fortune 500 companies to reduce healthcare procurement costs for their employees by having HMOs compete in open electronic auctions. Although the auctions were successful in generating significant cost savings for the companies in the first year, i.e., 1999, they failed to replicate the success and were eventually discontinued after two more years. Over the past decade since the failed auction experiment, effective utilization of information technologies have led to significant advances in the design of complex electronic markets. Using this knowledge, and data from the auctions, we point out several shortcomings of the auction design that, we believe, led to the discontinuation of the market after three years. Based on our analysis, we propose several actionable recommendations that policy makers can use to design a sustainable electronic market for procuring health insurance.     

From the Cover: Convolutional networks for fast,energy-efficient neuromorphic computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.The human brain is capable of remarkable acts of perception while consuming very little energy. The dream of brain-inspired computing is to build machines that do the same, requiring high-accuracy algorithms and efficient hardware to run those algorithms. On the algorithm front, building on classic work on backpropagation (1), the neocognitron (2), and convolutional networks (3), deep learning has made great strides in achieving human-level performance on a wide range of recognition tasks (4). On the hardware front, building on foundational work on silicon neural systems (5), neuromorphic computing, using novel architectural primitives, has recently demonstrated hardware capable of running 1 million neurons and 256 million synapses for extremely low power (just 70 mW at real-time operation) (6). Bringing these approaches together holds the promise of a new generation of embedded, real-time systems, but first requires reconciling key differences in the structure and operation between contemporary algorithms and hardware. Here, we introduce and demonstrate an approach we call Eedn, energy-efficient deep neuromorphic networks, which creates convolutional networks whose connections, neurons, and weights have been adapted to run inference tasks on neuromorphic hardware.For structure, typical convolutional networks place no constraints on filter sizes, whereas neuromorphic systems can take advantage of blockwise connectivity that limits filter sizes, thereby saving energy because weights can now be stored in local on-chip memory within dedicated neural cores. Here, we present a convolutional network structure that naturally maps to the efficient connection primitives used in contemporary neuromorphic systems. We enforce this connectivity constraint by partitioning filters into multiple groups and yet maintain network integration by interspersing layers whose filter support region is able to cover incoming features from many groups by using a small topographic size (7).For operation, contemporary convolutional networks typically use high precision ( ≥ 32-bit) neurons and synapses to provide continuous derivatives and support small incremental changes to network state, both formally required for backpropagation-based gradient learning. In comparison, neuromorphic designs can use one-bit spikes to provide event-based computation and communication (consuming energy only when necessary) and can use low-precision synapses to colocate memory with computation (keeping data movement local and avoiding off-chip memory bottlenecks). Here, we demonstrate that by introducing two constraints into the learning rule—binary-valued neurons with approximate derivatives and trinary-valued ({−1,0,1}) synapses—it is possible to adapt backpropagation to create networks directly implementable using energy efficient neuromorphic dynamics. This approach draws inspiration from the spiking neurons and low-precision synapses of the brain (8) and builds on work showing that deep learning can create networks with constrained connectivity (9), low-precision synapses (10, 11), low-precision neurons (12–14), or both low-precision synapses and neurons (15, 16). For input data, we use a first layer to transform multivalued, multichannel input into binary channels using convolution filters that are learned via backpropagation (12, 16) and whose output can be sent on chip in the form of spikes. These binary channels, intuitively akin to independent components (17) learned with supervision, provide a parallel distributed representation to carry out high-fidelity computation without the need for high-precision representation.Critically, we demonstrate that bringing the above innovations together allows us to create networks that approach state-of-the-art accuracy performing inference on eight standard datasets, running on a neuromorphic chip at between 1,200 and 2,600 frames/s (FPS), using between 25 and 275 mW. We further explore how our approach scales by simulating multichip configurations. Ease-of-use is achieved using training tools built from existing, optimized deep learning frameworks (18), with learned parameters mapped to hardware using a high-level deployment language (19). Although we choose the IBM TrueNorth chip (6) for our example deployment platform, the essence of our constructions can apply to other emerging neuromorphic approaches (20–23) and may lead to new architectures that incorporate deep learning and efficient hardware primitives from the ground up.