Local Block Research Articles

Graph-Based Non-Sampling for Knowledge Graph Enhanced Recommendation

Knowledge graph (KG) enhanced recommendation, which aims to solve the cold start and explainability in recommender systems, has attracted considerable research interest recently. Existing recommender systems usually focus on implicit feedback such as purchase history without negative feedback. Most of them apply the negative sampling strategy to deal with the implicit feedback data, which may ignore the latent positive user-item interaction. Some other works adopt the non-sampling strategy that treats all non-observed interactions as negative samples and assigns a weight for each negative sample to represent the probability that this sample is a positive sample. However, they use a simple and intuitive weight assignment strategy and cannot catch the latent relationship from all interaction data. To address these problems, we consider graph structure information of both user-item interaction and knowledge graph, and propose a Graph-based Non-Sampling strategy to achieve efficient performance in Knowledge graph enhanced Recommendation (GNSKR). GNSKR utilizes node centrality to significantly improve recommendation performance with low computation cost. Meanwhile, we combine knowledge graph embedding and recommendation task with a local aggregation block, which efficiently catches the high-order connection information in KG enhanced recommendation. Experiments on three datasets show that GNSKR embraces the state-of-the-art with competitive efficiency.

Nasal Bone Fracture Reduction Under Local Anaesthesia: A Holistic Approach to Nasal Blocks and a Comparison with General Anaesthesia.

To compare the pain scores in closed reduction of nasal bone fractures under local anaesthesia (LA) and general anaesthesia (GA), and to outline the blocks that should be used for the same based on their nerve supply. A prospective study was conducted with 40 patients with Class 1 and 2 nasal bone and septal fracture. 20 patients underwent the procedure under LA and 20 under GA. The local blocks that should be used based on the complete nerve supply of the nose has been tabulated. Pain scores were recorded immediately after the procedure and 6h later. Additionally, all the patients undergoing reduction under LA were asked, if given the choice again, if they would prefer to undergo the procedure under LA or GA. The overall difference in the pain scores calculated by T-test showed a p-value of 0.08807 (the result was not significant at p < .05) in the immediate post operative period. At the 6h post operative period, overall difference in the pain scores showed a p-value of 0.384972 (not significant at p < .05). Of the patients who underwent the procedure under LA, 18 of 20 (90%) said that if given a choice again, they would undergo the procedure under LA, while 2 said they would prefer GA. Based on the pain score in the La vs. GA groups, there is no significant difference in the pain scores whether closed reduction is done under local or general anaesthesia. If all the blocks are given keeping the nerve supplies in mind, and both externally, and with pledgets, the entire nerve supply of the nose can be blocked.

Multi-Attention Multi-Image Super-Resolution Transformer (MAST) for Remote Sensing

Deep-learning-driven multi-image super-resolution (MISR) reconstruction techniques have significant application value in the field of aerospace remote sensing. In particular, Transformer-based models have shown outstanding performance in super-resolution tasks. However, current MISR models have some deficiencies in the application of multi-scale information and the modeling of the attention mechanism, leading to an insufficient utilization of complementary information in multiple images. In this context, we innovatively propose a Multi-Attention Multi-Image Super-Resolution Transformer (MAST), which involves improvements in two main aspects. Firstly, we present a Multi-Scale and Mixed Attention Block (MMAB). With its multi-scale structure, the network is able to extract image features from different scales to obtain more contextual information. Additionally, the introduction of mixed attention allows the network to fully explore high-frequency features of the images in both channel and spatial dimensions. Secondly, we propose a Collaborative Attention Fusion Block (CAFB). By incorporating channel attention into the self-attention layer of the Transformer, we aim to better establish global correlations between multiple images. To improve the network’s perception ability of local detailed features, we introduce a Residual Local Attention Block (RLAB). With the aforementioned improvements, our model can better extract and utilize non-redundant information, achieving a superior restoration effect that balances the global structure and local details of the image. The results from the comparative experiments reveal that our approach demonstrated a notable enhancement in cPSNR, with improvements of 0.91 dB and 0.81 dB observed in the NIR and RED bands of the PROBA-V dataset, respectively, in comparison to the existing state-of-the-art methods. Extensive experiments demonstrate that the method proposed in this paper can provide a valuable reference for solving multi-image super-resolution tasks for remote sensing.

Adjunctive dexamethasone palmitate use for intercostal nerve block after video-assisted thoracoscopic surgery: A prospective, randomized control trial

ObjectivesThe efficacy of dexamethasone palmitate in extending durations of local anesthetic blocks is uncertain. In a randomized, double-blind study of patients undergoing video-assisted thoracoscopic surgery, we tested whether intravenous or perineural dexamethasone palmitate caused prolonged analgesia after intercostal nerve block. MethodsA total of 90 patients subjected to video-assisted thoracoscopic surgery between May and December 2022 were randomly assigned to one of three intercostal nerve blocks study arms (n = 30 each), requiring the addition of 0.5% ropivacaine (23 ml) as follows: controls (C group), 2 ml saline; IV-DXP group, 2 ml saline + 2 ml (8 mg) intravenous dexamethasone palmitate; and PN-DXP group, 2 ml (8 mg) perineural dexamethasone palmitate. Time to first postoperative remedial analgesia served as primary outcome measure. Secondary endpoints included postoperative opioid consumption, pain scores by Visual Analog Scale, analgesia satisfaction, and related adverse effects. ResultsCompared with controls or the IV-DXP group, time to first postoperative remedial analgesia was longer and postoperative opioid consumption for rescue analgesia was lower in the PN-DXP group (p < 0.01). Similarly, the Visual Analog Scale scores in patients at 8, 12, 18, and 24 h postoperatively were lower in the PN-DXP group than in controls and the IV-DXP group (p < 0.01). Patient satisfaction was statistically lower in the PN-DXP group, compared with either the control or IV-DXP group (p < 0.05). Clinically, the three groups did not differ significantly in occurrences of adverse effects during the 48-h postoperative monitoring period (p > 0.05). ConclusionsPerineural dexamethasone palmitate is a promising adjunct to ropivacaine intercostal nerve block by prolonging analgesia with almost no related adverse effects.

A critical reappraisal of paleomagnetic evidence for Philippine Sea Plate rotation

The kinematic history of the Philippine Sea Plate (PSP) is crucial for interpreting its geological record related to subduction initiation processes and the paleogeography of the junction between the Paleo-Pacific and Tethyan oceanic realms. However, reconstructing PSP's kinematic history is difficult because the plate has been surrounded by subduction zones for most of its history. In absence of marine magnetic anomalies to constrain PSP's motion relative to its neighboring plates, paleomagnetic data may be used as quantitative constraints on its motion. Previous paleomagnetic studies interpreted easterly deflected declinations to infer clockwise rotations of up to 90° since the Eocene. However, rotations inferred from these datasets may also reflect local block rotations related to plate margin deformation. We here re-evaluate to what extent paleomagnetic data from the PSP unequivocally demonstrate plate motion rather than local rotation. To this end, we provide new data from Guam, in the Mariana forearc, and reassess published paleomagnetic data. Our new data from Guam come from two localities in the Eocene, two in the Oligocene, and two in the Miocene. Our compilation assesses data quality against recently defined criteria. Our new results demonstrate that in Guam, declination differences of up to 35° exist in rocks of Eocene age, indicating local rotations. Our compilation identifies both clockwise and counterclockwise rotations from the plate margins, with little confidence which of these would reflect plate-wide rotation. We compiled paleolatitude data from igneous rocks, which we correct for microplate rotation constrained by intra-PSP marine magnetic anomalies and show a northward drift of the PSP of ∼15° since the Eocene, but without a paleomagnetic necessity for major vertical axis rotation. Hence, with the currently available data, rotations of the PSP may be permitted, but are not required. Plate motion is currently better reconstructed from geological constraints contained in circum-PSP orogenic belts.

Attention-guided and fine-grained feature extraction from face images for gaze estimation

Research on appearance gaze estimation based on deep learning has achieved considerable results. However, a lack of information in the extracted local blocks reduces the precision of gaze estimation since CNNs do not prioritize the information within the key picture blocks for face image estimation. In this research, fine-grained visual information is extracted from local blocks using a Transformer in Transformer (TNT) model that emphasizes the interactions between local blocks. First, TNT-based gaze estimation models (GTiT-Pure and GTiT-Hybrid) are established with the TNT model to capture both coarse- and fine-grained visual information about the face, which are then combined to provide a feature representation for gaze regression. Then, the performance of the two models are evaluated on four gaze estimation datasets. Experimental results demonstrate that the pre-trained GTiT-Pure has less gaze estimation error than the majority of CNNs and Transformer models, and that the GTiT-Hybrid model performs the best on the EyeDiap and RT-Gene datasets. The GTiT model which combines both coarse- and fine-grained gaze features in face images, can be used to further explore the advantages of the Transformer model in gaze feature extraction.

Two-stream regression network for dental implant position prediction

In implant prosthesis treatment, the design of the surgical guide heavily relies on the manual location of the implant position, which is subjective and prone to doctor’s experiences. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global–local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods.

Enhanced Recovery After Surgery (ERAS®) Society Guidelines for Gynecologic Oncology: Addressing Implementation Challenges - 2023 Update

ABSTRACT Implementation of the Enhanced Recovery after Surgery (ERAS) Society Guidelines in 2016 has resulted in an approximate decrease in hospital length of stay (LOS), reduction in complications and readmission, and cost saving. Recent publications however have demonstrated that barriers and gaps in understanding may result in many ERAS recommendations being poorly adhered to in gynecologic oncology patients. The aims of this second update to the ERAS Society Guidelines for gynecologic oncology is to summarize and update the evidence investigating specific implementation challenges to increase ERAS uptake and improve clinical outcomes for patients. A literature search was conducted for topics specified to 9 ERAS implementation challenges identified: perioperative oral intake safety, preoperative medication importance, penicillin allergies, intraoperative analgesia, how to manage urinary drainage, venous thromboembolism prophylaxis, postoperative opioid prescription practices, same-day discharge programs, and how can I overcome barriers to ERAS implementation. The quality of evidence and recommendations was evaluated, and recommendations graded as strong or weak based on quality, balance between desirable and undesirable effects on holistic clinical outcomes, and on values and preferences of practitioners. Regarding perioperative oral intake safety, patients should be encouraged to consume clear liquids in 2 hours before surgery and preoperative carbohydrate loading improves satisfaction and comfort. Optimal preoperative medications include nonsteroidal anti-inflammatory drugs, acetaminophen, and gabapentinoids. Patients with penicillin allergies should receive standard surgical antibiotic prophylaxis. For intraoperative analgesia, wound infiltration with local anesthetic and TAP block are preferred over thoracic epidural analgesia. Indwelling bladder catheters should be removed as early as possible in the postoperative period such as on the day of surgery for minimally invasive procedures and no later than postoperative day 1 for laparotomy. Patients at increased risk of venous thromboembolism should receive dual prophylaxis with compression and medications, and extended chemoprophylaxis should be prescribed to those with high-risk criteria or after laparotomy. Multimodal opioid-reduction is critical for all patients, and decreased postdischarge opioid proscribing is improved with a team approach and does not affect pain control or patient satisfaction. Patient controlled analgesia is a last resort for those requiring repeated treatment with intravenous opioids. Multidisciplinary same-day discharge programs should be considered in minimally invasive surgeries, and implementation requires a multidisciplinary team approach with patient education, case selection, and ERAS principles. Overcoming barriers to ERAS principles is more successful with changes at the management level and education. Communication of the economic benefits of ERAS to administrators may be a strong incentive to garner support. Although the evidence level varied between moderate and strong for each of these recommendations, the recommendation grade for each was strong. This updated ERAS guideline focuses on addressing implementation challenges and controversial aspects of ERAS defined by a stakeholder clinician group and offers suggestions based on objective data for overcoming these barriers.

TSD-Depth: Using transformers and self-distilling for self-supervised indoor depth estimation

Supervised monocular depth estimation has always been one of the most important tasks in computer vision. With the convolution module as a basic operator, the U-shaped network architecture has become the de facto standard and has achieved tremendous success. However, due to the limited receptive field of the convolution operation, the CNNs are generally inferior in explicitly modeling the long-range dependencies. Originally proposed for natural language processing, the transformers are designed for performing sequence-to-sequence predictions based on global self-attention mechanism. Therefore, the transformers can capture long-range dependencies. However, they have limited localization abilities due to insufficient low-level details. In this work, we propose a TSD-Depth model, which merits both the transformers and the CNNs, as a strong alternative for self-supervised monocular depth estimation. The proposed model simultaneously extracts the global contextual information and local spatial detail features. Furthermore, by designing the hybrid encoder connection method and proper-sized transformer module, the global and local information can more effectively interact. In addition, a local multi-scale fusion block is first proposed to refine the fine-grained details. More importantly, the knowledge is learned by using self-distillation to skip the multi-scale fusion block concatenated with the encoder at the inference time, computed only during the training process for minimal overhead. The experimental results on NYU-v2 and ScanNet datasets show that the proposed TSD-Depth achieves the best performance as compared to the previous state-of-the-art methods.

A Comparative Study between Postoperative Pain Relief with Transversus Abdominis Plane Block and Local Field Block at Port Site in Lower Abdominal Laparoscopic Surgeries

Background: In order to provide post-operative analgesia following abdominal incision, blocking the sensory nerve supply to the anterior abdominal wall is a potential strategy. Current methods for blocking these afferent nerves, including abdominal field blocks, have limited therapeutic usefulness, and the level of block that is accomplished is variable. Aim: To compare the postoperative pain relief between ultrasound guided TAP block and local field block at port site in lower abdominal laparoscopic surgeries, the pain being assessed by visual analogue scale scoring. Method and materials: Sixty patients were considered in the study, 30 patients were randomized to undergo TAP blockade and 30 were randomized to undergo local field blockade. Results: Fentanyl consumption as total requirement of fentanyl, number of patients requiring fentanyl and incidence of nausea and vomiting. P value of ≤ 0.05 was considered to be significant. ANOVA test for age reveals the P value of 0.643 indicating that there is no significant effect of the variance in the age of patients between the groups. ANOVA for height shows a P value of 0.076, P value for weight of 0.176 and P value for ASA grading of 0.798 shows that there is no significance of the differences in the patient characteristics. Mann Whitney test, t-test, modified t-test were the statistical hypothesis tests used to assess significance of difference in patient characteristics. Conclusion: The TAP block holds considerable promise as part of a multimodal analgesic regimen for post-operative pain management in lower abdominal surgeries.

Opioid-Sparing Pain Control After Rhinoplasty: Updated Review of the Literature.

Rhinoplasty is one of the most performed elective surgeries, and given the opioid crisis, increasing research and studies are focused on successful pain control with multimodality opioid-sparing techniques, such as acetaminophen, non-steroidal anti-inflammatory drugs, and gabapentin. Although limiting overuse of opioids is critical, this cannot be at the expense of inadequate pain control, particularly as insufficient pain control can be correlated with patient dissatisfaction and the postoperative experience in elective surgery. There is likely significant opioid overprescription, as patients often report taking less than 50% of their prescribed opioids. Furthermore, excess opioids provide opportunities for misuse and opioid diversion if not disposed of properly. To optimize postoperative pain control and minimize opioid requirements, interventions must occur at the preoperative, intraoperative, and postoperative timepoints. Preoperative counseling is imperative to set expectations for pain and to screen for predisposing factors for opioid misuse. Use of local nerve blocks and long-acting analgesia in conjunction with modified surgical techniques can lead to prolonged pain control. Postoperatively, pain should be managed with a multimodal approach, incorporating acetaminophen, NSAIDs, and potentially gabapentin with opioids reserved for rescue analgesia. Rhinoplasty represents a category of short-stay, low/medium pain, and elective procedures highly susceptible to overprescription and consequently, are readily amenable to opioid minimization through standardized perioperative interventions. Recent literature on regimens and interventions to help limit opioids after rhinoplasty are reviewed and discussed here.

Online distributed optimization with strongly pseudoconvex-sum cost functions and coupled inequality constraints

In this paper, the problem of online distributed optimization with coupled inequality constraints is studied by employing multi-agent systems. Each agent only has access to the information associated with its own cost function and a local block of the constraint function, and can exchange local information with its immediate neighbors via a time-varying digraph. Moreover, the information of current cost functions and constraint functions is not available to agents until decisions are made. Of particular interest is that the cost function is considered to be strongly pseudoconvex. To handle this problem, an auxiliary optimization-based online distributed primal–dual algorithm is proposed. The performance of the algorithm is measured by the dynamic regret and the constraint violation. Under mild assumptions on graphs, we prove that if the cumulative deviation of minimizer sequence grows within a certain rate, then both the dynamic regret and the violation of coupled inequality constraints grow sublinearly. Finally, a simulation example is given to corroborate the validity of our results.

PHCU-Net: A parallel hierarchical cascade U-Net for skin lesion segmentation

Melanoma is a malignant tumor condition which can be successfully cured with early detection and treatment. Currently, deep learning is one of the research hotspots in automatic diagnosis, with two primary categories of medical image segmentation applications: Convolution Neural Network (CNN) methods such as U-Net and Transformer-based methods. Despite achieving remarkable performance in image segmentation, both above approaches have some inherent shortcomings that cannot be overlooked. The convolution operation in the CNN structure cannot adequately capture global dependencies, leading to a major negative impact on segmentation performance. While the Multi-head Self-Attention mechanism in Transformer can efficiently extract extensive global features, its large computational complexity and lack of local induction bias cannot be ignored. These factors result in a significant reduction in the precision of clinical diagnosis using automated segmentation. To address the aforementioned issues, this paper proposes a U-shaped network structure called PHCU-Net which comprises global feature block, local feature block and dual-branch hierarchical attention mechanism that is designed specifically for the segmentation network of skin lesions. In addition, we argue that the traditional skip connection can be further improved to acquire stronger contexts by simply incorporating convolution attention, so that the decoder can fully collect the global and local feature information. The aforementioned innovative work empowers our model to generate superior skin lesion segmentation results compared to other classical algorithms and deep learning-based models. Extensive experimental results on three open-source datasets (ISIC2017, ISIC2018, and PH2) demonstrate the effectiveness of the proposed PHCU-Net in melanoma lesion segmentation.

Commutators of Hardy-Littlewood operators on p-adic function spaces with variable exponents

Abstract In this article, we obtain some sufficient conditions for the boundedness of commutators of p p -adic Hardy-Littlewood operators with symbols in central bounded mean oscillation space and Lipschitz space on the p p -adic function spaces with variable exponents such as the p p -adic local central Morrey, p p -adic Morrey-Herz, and p p -adic local block spaces with variable exponents.

Redução de prolapso vaginal em uma porca SRD no município de São Francisco do Guaporé - RO – relato de caso

Vaginal prolapse consists of the relaxation of the vagina's attachment to the pelvic cavity, allowing the mucosa to be exposed through the vulvar rhyme; this disease affects a wide variety of animals of zootechnical interest. It is considered a multifactorial condition influenced by several factors, such as management, environment, nutrition and hygiene. The objective is to report a mixed breed sow, 80 kg, primiparous, raised in the intensive system, with balanced ration, which presented vaginal prolapse three days after birth. Dissociative anesthesia was used with ketamine at a dose of 30 mg/kg/IM and acepromazine at a dose of 1 mg/kg/IM, and local block with 3 ml of lidocaine. Dissociative anesthesia with ketamine proved to be effective in terms of anesthesia, promoting good analgesia and not causing any reaction in the animal that could affect its maternal instinct; the suture used was Wolf captonated and after the surgical process, the animal remained in the herd until the piglets were weaned.

Day microsurgery: Further application of free flap transfer as an ambulatory surgery

Free flap transfer (FFT) is essential in soft tissue reconstruction and can be done under local anesthesia. However, there is no study evaluating the feasibility of FFT without hospitalization. This study evaluated the feasibility of day microsurgery-FFT as a day surgery without hospitalization. Patients who underwent day microsurgery were included. The FFT was performed under local infiltration and/or block anesthesia without sedation by a surgeon with enough experience in supermicrosurgery. We focused on minimal dissection and secure hemostasis to prevent possible complications. Patient characteristics, operative findings, and postoperative course were evaluated. Seventeen patients with a mean age of 40.4 years were included. All defects were of the upper extremity and due to trauma. Utilized anesthesia included local infiltration in 11 sites, digital block in 10 sites, wrist block in 12 sites, and elbow block in 4 sites. Used flap included superficial circumflex iliac artery perforator flap in 8 (47.1%) cases and short-pedicle partial toe flaps in 9 (52.9%) cases. The mean diameters of the anastomosed artery/vein were 0.88/1.29mm, and the mean operation time was 68.9min. All transferred flaps survived without total or partial necrosis. The postoperative course was uneventful except for 1 (5.9%) case with minor wound dehiscence. FFT could safely be performed without hospitalization in selected cases of an upper extremity defect. Supermicrosurgery and careful patient selection play an important role in safe day microsurgery.

MIMO Is All You Need：A Strong Multi-in-Multi-Out Baseline for Video Prediction

The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner. This way often leads to severe performance degradation when they try to extrapolate a longer period of future, thus limiting the practical use of the prediction model. Alternatively, a Multi-In-Multi-Out (MIMO) architecture that outputs all the future frames at one shot naturally breaks the recursive manner and therefore prevents error accumulation. However, only a few MIMO models for video prediction are proposed and they only achieve inferior performance due to the date. The real strength of the MIMO model in this area is not well noticed and is largely under-explored. Motivated by that, we conduct a comprehensive investigation in this paper to thoroughly exploit how far a simple MIMO architecture can go. Surprisingly, our empirical studies reveal that a simple MIMO model can outperform the state-of-the-art work with a large margin much more than expected, especially in dealing with long-term error accumulation. After exploring a number of ways and designs, we propose a new MIMO architecture based on extending the pure Transformer with local spatio-temporal blocks and a new multi-output decoder, namely MIMO-VP, to establish a new standard in video prediction. We evaluate our model in four highly competitive benchmarks. Extensive experiments show that our model wins 1st place on all the benchmarks with remarkable performance gains and surpasses the best SISO model in all aspects including efficiency, quantity, and quality. A dramatic error reduction is achieved when predicting 10 frames on Moving MNIST and Weather datasets respectively. We believe our model can serve as a new baseline to facilitate the future research of video prediction tasks. The code will be released.

CGC-Net: A Context-Guided Constrained Network for Remote-Sensing Image Super Resolution

In remote-sensing image processing tasks, images with higher resolution always result in better performance on downstream tasks, such as scene classification and object segmentation. However, objects in remote-sensing images often have low resolution and complex textures due to the imaging environment. Therefore, effectively reconstructing high-resolution remote-sensing images remains challenging. To address this concern, we investigate embedding context information and object priors from remote-sensing images into current deep learning super-resolution models. Hence, this paper proposes a novel remote-sensing image super-resolution method called Context-Guided Constrained Network (CGC-Net). In CGC-Net, we first design a simple but effective method to generate inverse distance maps from the remote-sensing image segmentation maps as prior information. Combined with prior information, we propose a Global Context-Constrained Layer (GCCL) to extract high-quality features with global context constraints. Furthermore, we introduce a Guided Local Feature Enhancement Block (GLFE) to enhance the local texture context via a learnable guided filter. Additionally, we design a High-Frequency Consistency Loss (HFC Loss) to ensure gradient consistency between the reconstructed image (HR) and the original high-quality image (HQ). Unlike existing remote-sensing image super-resolution methods, the proposed CGC-Net achieves superior visual results and reports new state-of-the-art (SOTA) performance on three popular remote-sensing image datasets, demonstrating its effectiveness in remote-sensing image super-resolution (RSI-SR) tasks.

Transverse abdominis plane block as a method of anesthesia after caesarean section

Although we have various methods of analgesia, the prevalence of severe pain after caesarean section remains high. Therefore, it is necessary to look for new reliable technologies for optimal postoperative anesthesia.The objective was to compare the efficiency of postoperative wound infiltration with a local anesthetic and transverse abdominis plane block (TAP-block) as a component of multimodal analgesia in the postoperative period after cesarean section.Materials and methods. A prospective randomized study was conducted. It included 91 patients after elective caesarean section under spinal anesthesia. Patients were divided into 3 groups depending on the method of postoperative analgesia. In group 1 (n = 30), we used wound analgesia. In group 2 (n = 32), transverse abdominis plane block was performed. In group 3 (n = 29), intravenous infusion of paracetamol in combination with intramuscular injection of tramadol 0.5 mg/kg were used for postoperative analgesia.Results. Pain syndrome was less severe after wound analgesia and TAP-block for 24 hours compared to analgesia with systemic analgesics. Moreover, during wound analgesia, lower points of the visual analogue scale (VAS) were traced for three days. The minimum consumption of paracetamol was on the background of wound analgesia, while no patient required the introduction of narcotic analgesics. Women in the wound analgesia group were able to earlier activating: they could walk after 5.6±0.2 hours, compared to 6.1± 0.2 hours with TAP-block and 8.8± 0.4 hours with analgesia with systemic analgesics.Conclusion. Continuous analgesia of postoperative wound is a safe and effective method that allows achieving adequate postoperative analgesia, avoiding using narcotic analgesics and reducing the consumption of non-narcotic systemic analgesics.

MHAN: Multi-Stage Hybrid Attention Network for MRI reconstruction and super-resolution

High-quality magnetic resonance imaging (MRI) affords clear body tissue structure for reliable diagnosing. However, there is a principal problem of the trade-off between acquisition speed and image quality. Image reconstruction and super-resolution are crucial techniques to solve these problems. In the main field of MR image restoration, most researchers mainly focus on only one of these aspects, namely reconstruction or super-resolution. In this paper, we propose an efficient model called Multi-Stage Hybrid Attention Network (MHAN) that performs the multi-task of recovering high-resolution (HR) MR images from low-resolution (LR) under-sampled measurements. Our model is highlighted by three major modules: (i) an Amplified Spatial Attention Block (ASAB) capable of enhancing the differences in spatial information, (ii) a Self-Attention Block with a Data-Consistency Layer (DC-SAB), which can improve the accuracy of the extracted feature information, (iii) an Adaptive Local Residual Attention Block (ALRAB) that focuses on both spatial and channel information. MHAN employs an encoder–decoder architecture to deeply extract contextual information and a pipeline to provide spatial accuracy. Compared with the recent multi-task model T2Net, our MHAN improves by 2.759 dB in PSNR and 0.026 in SSIM with scaling factor ×2 and acceleration factor 4× on T2 modality.