Study of the features of coronary artery atheromatous plaque using intravascular ultrasound in patients with impaired glucose tolerance

AbstractObjectiveWe used intravascular ultrasound (IVUS) to analyze the features of coronary artery atheromatous plaque in patients with impaired glucose tolerance and mild-to-moderate angiographic coronary stenosis. The aim was to determine the clinical significance of plaque characteristics as well as the relationship between hemoglobin A1c (HbA1c) levels and coronary artery lesions.MethodsHbA1c levels were evaluated in 85 patients (96 lesions), of whom 46 had impaired glucose tolerance (IGT Group) and 39 had normal blood glucose (NBG Group). IVUS was used to analyze the lesion vessel of both groups qualitatively and quantitatively. The external elastic membrane area (EEMA), minimal lumen area (MLA), plaque area (PA), and plaque burden (PB) were measured for both the target lesion and the reference segments (reference external elastic membrane area (REEMA), reference minimal lumen area (RMLA), reference plaque area (RPA), and reference plaque burden (RPB), respectively).ResultsHbA1c levels were significantly higher in the IGT Group than in the NBG Group (P < 0.05). In the IGT Group there was more soft plaque, eccentric plaque, and positive remodeling, and less calcification, while in the NBG Group there was much harder plaque and calcification, no reconstruction, and negative remodeling (P < 0.05). MLA was smaller in the IGT Group than in the NBG Group, while EEMA, PA, and PB were clearly greater (P < 0.05). In the meantime, RMLA was clearly smaller in the IGT Group than in the NBG Group, while RPA and RPB were greater (P < 0.05). HbA1c levels were positively correlated with PA and PB, and negatively correlated with MLA.ConclusionIVUS is very valuable for the evaluation of mild-to-moderate coronary lesions. The coronary artery lesions in patients with IGT are more serious and widespread than those in patients with NBG. HbA1c levels might be of some value in assessing the severity of coronary artery lesions

Genome sequence and organization of a nucleopolyhedrovirus that infects the tea looper caterpillar, Ectropis obliqua

AbstractThe complete nucleotide sequence of Ectropis obliqua nucleopolyhedrovirus (EcobNPV), which infects the tea looper caterpillar, was determined and analyzed. The double stranded circular genome is composed of 131,204 bp and is 37.6% G+C rich. The analysis predicted 126 putative, minimally overlapping open reading frames (ORFs) with 150 or more nucleotides that together compose 89.8% of the genome. The remaining 10.2% constitute non-coding and three homologous regions. Comparison with previously sequenced baculoviruses indicated that three ORFs were unique to EcobNPV, while the remaining 123 ORFs shared identity with other baculovirus genes. In addition to two bro homologues, three other repeat ORFs, including dbp, p26, and odv-e66, were identified. Phylogenetic analysis indicated that each member of the paired ORFs was acquired independently. Gene parity plot analysis and percent identity of gene homologues suggested that EcobNPV is a Group II NPV, although its genomic organization was highly distinct

Analysis of adverse drug reactions of Denosumab (Prolia) in osteoporosis based on FDA adverse event reporting system (FAERS)

ObjectiveTo comprehensively analyze the ADRs associated with Denosumab (Prolia) in the treatment of osteoporosis using data from the FAERS database, and gain a better understanding of the potential risks and side effects of Denosumab (Prolia) therapy.MethodsData of Denosumab (Prolia) were collected from the FAERS database covering the period from first quarter of 2010 to the third quarter of 2023. Disproportionality analysis was performed by calculating the reporting odds ratios (ROR), proportional reporting ratio (PRR), and Bayesian analysis confidence propagation neural network (BCPNN) to detect positive signals.ResultsTotally, 17,985,365 reports were collected from the FAERS database, 1,97,807 reports of Denosumab (Prolia) were identified as the “primary suspected (PS)” ADRs. Denosumab (Prolia) induced ADRs occurred in 27 organ systems. 38 significant disproportionality PTs satisfying with the three algorithms were retained at the same time. Unexpected significant ADRs such as bone density abnormal and immobile also occur. The majority of the ADRs occurred within the first 30 days after Denosumab (Prolia) initiation.ConclusionBased on the American FAERS database, the high frequency ADRs of Denosumab (Prolia) were hypocalcaemia, bone density abnormal, eczema, rebound effect, spinal deformity, etc. Clinical use of this drug should focus on this part of ADRs. Attention should also be paid to newly discovered ADRs, such as immobile, menopausal symptoms, etc., to avoid more serious consequences. Cohort studies, more detailed and comprehensive case information, and long-term clinical investigations are needed to confirm these results and to further understand the safety profile of Denosumab (Prolia)

High reward enhances perceptual learning.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications

Robot sensor calibration via neural network and particle swarm optimization enhanced with crossover and mutation

U cilju određivanja položaja i orijentacije nekog predmeta u zglobu za robot, treba procijeniti odnos transformacije sustava ruka-oko, što se opisuje kao rotacijska matrica i vektor translacije. Predlaže se novi pristup koji integrira neuronsku mrežu i algoritam optimaizacije roja čestica s operacijom križanja i mutacije za kalibraciju osjećaja robota. Najprije se strukturira neuronska mreža s matricom rotacijske težine gdje su težine elementi rotacijskog dijela homogenog prijenosa sustava ruka-oko. Tada se algoritam optimalizacije roja čestica integrira u program rješavanja, gdje se faktori težine inercije i vjerojatnosti mutacije sami podešavaju prema putanji gibanja čestica u longitudinalnom pravcu i lateralnom pravcu. Kad je zadovoljen kriterij terminacije, rotaciona matrica se dobiva iz nepromjenljivih težina neuronske mreže. Tada se rješava vektor translacije i postiže se položaj i orijentacija slike s kamere u odnosu na sliku sa zgloba. Predloženi pristup pruža novu šemu za kalibraciju robota tehnikom samo-adaptacije, što garantira ortogonalnost riješenih rotacijskih komponenti homogenog transforma.In order to determine the position and orientation of an object in the wrist frame for robot, transform relation of hand-eye system should be estimated, which is described as rotational matrix and translational vector. A new approach integrating neural network and particle swarm optimization algorithm with crossover and mutation operation for robot sense calibration is proposed. First the neural network with rotational weight matrix is structured, where the weights are the elements of rotational part of homogeneous transform of the hand-eye system. Then the particle swarm optimization algorithm is integrated into the solving program, where the inertia weight factor and mutation probability are tuned self-adaptively according to the motion trajectory of particles in longitudinal direction and lateral direction. When the termination criterion is satisfied, the rotational matrix is obtained from the neural network’s stable weights. Then the translational vector is solved, so the position and orientation of camera frame with respect to wrist frame is achieved. The proposed approach provides a new scheme for robot sense calibration with self-adaptive technique, which guarantees the orthogonality of solved rotational components of the homogeneous transform

Robot sensor calibration via neural network and particle swarm optimization enhanced with crossover and mutation

U cilju određivanja položaja i orijentacije nekog predmeta u zglobu za robot, treba procijeniti odnos transformacije sustava ruka-oko, što se opisuje kao rotacijska matrica i vektor translacije. Predlaže se novi pristup koji integrira neuronsku mrežu i algoritam optimaizacije roja čestica s operacijom križanja i mutacije za kalibraciju osjećaja robota. Najprije se strukturira neuronska mreža s matricom rotacijske težine gdje su težine elementi rotacijskog dijela homogenog prijenosa sustava ruka-oko. Tada se algoritam optimalizacije roja čestica integrira u program rješavanja, gdje se faktori težine inercije i vjerojatnosti mutacije sami podešavaju prema putanji gibanja čestica u longitudinalnom pravcu i lateralnom pravcu. Kad je zadovoljen kriterij terminacije, rotaciona matrica se dobiva iz nepromjenljivih težina neuronske mreže. Tada se rješava vektor translacije i postiže se položaj i orijentacija slike s kamere u odnosu na sliku sa zgloba. Predloženi pristup pruža novu šemu za kalibraciju robota tehnikom samo-adaptacije, što garantira ortogonalnost riješenih rotacijskih komponenti homogenog transforma.In order to determine the position and orientation of an object in the wrist frame for robot, transform relation of hand-eye system should be estimated, which is described as rotational matrix and translational vector. A new approach integrating neural network and particle swarm optimization algorithm with crossover and mutation operation for robot sense calibration is proposed. First the neural network with rotational weight matrix is structured, where the weights are the elements of rotational part of homogeneous transform of the hand-eye system. Then the particle swarm optimization algorithm is integrated into the solving program, where the inertia weight factor and mutation probability are tuned self-adaptively according to the motion trajectory of particles in longitudinal direction and lateral direction. When the termination criterion is satisfied, the rotational matrix is obtained from the neural network’s stable weights. Then the translational vector is solved, so the position and orientation of camera frame with respect to wrist frame is achieved. The proposed approach provides a new scheme for robot sense calibration with self-adaptive technique, which guarantees the orthogonality of solved rotational components of the homogeneous transform