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In recent years, phase linking (PL) methods in radar time-series interferometry (TSI) have proven to be powerful tools in geodesy and remote sensing, enabling the precise monitoring of surface displacement and deformation. While these methods are typically designed to operate on a complete network of interferograms, generating such networks is often challenging in practice. For instance, in non-urban or vegetated regions, decorrelation effects lead to significant noise in long-term interferograms, which can degrade the time-series results if included. Additionally, practical issues such as gaps in satellite data, poor acquisitions, or systematic errors during interferogram generation can result in incomplete networks. Furthermore, pre-existing interferogram networks, such as those provided by systems like COMET-LiCSAR, often prioritize short temporal baselines due to the vast volume of data generated by satellites like Sentinel-1. As a result, complete interferogram networks may not always be available. Given these challenges, it is critical to understand the applicability of PL methods on these incomplete networks. This study evaluated the performance of two PL methods, eigenvalue decomposition (EVD) and eigendecomposition-based maximum-likelihood estimator of interferometric phase (EMI), under various network configurations including short temporal baselines, randomly sparsified networks, and networks where low-coherence interferograms have been removed. Using two sets of simulated data, the impact of different network structures on the accuracy and quality of the results was assessed. These patterns were then applied to real data for further comparison and analysis. The findings demonstrate that while both methods can be effectively used on short temporal baselines, their performance is highly sensitive to network sparsity and the noise introduced by low-coherence interferograms, requiring careful parameter tuning to achieve optimal results across different study areas.

In this work, a supervised learning rule based on Temporal Single Spike Coding for Effective Transfer Learning (TS4TL) is presented, an efficient approach for training multilayer fully connected Spiking Neural Networks (SNNs) as classifier blocks within a Transfer Learning (TL) framework. A new target assignment method named as “Absolute Target” is proposed, which utilizes a fixed, non-relative target signal specifically designed for single-spike temporal coding. In this approach, the firing time of the correct output neuron is treated as the target spike time, while no spikes are assigned to the other neurons. Unlike existing relative target strategies, this method minimizes computational complexity, reduces training time, and decreases energy consumption by limiting the number of spikes required for classification, all while ensuring a stable and computationally efficient training process. By seamlessly integrating this learning rule into the TL framework, TS4TL effectively leverages pre-trained feature extractors, demonstrating robust performance even with limited labelled data and varying data distributions. The proposed learning rule scales efficiently across both shallow and deep network architectures while maintaining consistent accuracy and reliability. Extensive evaluations on benchmark datasets highlight the strength of this approach, achieving state-of-the-art accuracies, including 98.91% on Eth80, surpassing previous works, and 91.89% on Fashion-MNIST, outperforming all fully connected structures in the literature. Additionally, high accuracies of 98.45% and 97.75% were recorded on the MNIST and Caltech101-Face/Bike datasets, respectively. Furthermore, TS4TL addresses a critical challenge by effectively reducing neuron misfires, ensuring that neurons respond correctly based on first-spike coding, a significant improvement over manually imposed solutions seen in prior works. These contributions collectively highlight the potential of TS4TL as a scalable and high-performance solution for temporal learning in SNNs.

Roux-en-Y gastric bypass (RYGB) is the second most common metabolic and bariatric surgery (MBS) globally. The impact of pouch size on weight loss outcomes and complications remains unclear. This study aims to compare the weight loss outcomes and complications in long pouch versus short pouch RYGB in patients with severe obesity. This retrospective study, conducted in 2021 in two academic tertiary Hospitals, included patients aged 18–65 with severe obesity who underwent RYGB with two different methods. Demographic data, past medical history, and surgical details were assessed. The study outcome was postoperative metrics at 12 months including weight loss outcomes and complications like marginal ulceration, and leaks. A total of 219 patients, who were included in this study, were divided into two groups: 107 with long gastric pouches and 112 with short gastric pouches. The average age was 41.33 ± 10.26 and 42.45 ± 11.70 in long and short gastric pouches, respectively. Patients with long gastric pouches had a mean weight of 113.29 ± 16.52 kg and mean Body Mass Index (BMI) of 42.97 ± 4.15 kg/m 2 , and patients with short gastric pouches had a mean weight of 118.39 ± 12.80 kg and mean BMI of 45.21 ± 5.10 kg/m 2 . At 12 months after surgery, substantial weight loss was noted in all participants (37.8 ± 10.7 kg in patients with long gastric pouch; 48.1 ± 11.3 kg in patients with short gastric pouch; P = 0.033). Delta BMI (P = 0.072), and TWL% (P = 0.061), were more pronounced in patients with short pouches, however the difference was not significant. Remission of underlying diseases and endoscopic findings were comparable for short and long gastric pouch groups. Both long and short-pouch gastric bypass surgeries are effective and safe for weight loss and remission of obesity-associated medical problems in patients with severe obesity and exhibited similar rates for remission of underlying diseases and endoscopic findings. More studies are needed to individualize surgical approaches based on patient characteristics.

There is no data on patients with severe obesity who developed coronavirus disease 2019 (COVID-19) after bariatric surgery. Four gastric bypass operations, performed in a 2-week period between Feb 24 and March 4, 2020, in Tehran, Iran, were complicated with COVID-19. The mean age and body mass index were 46 ± 12 years and 49 ± 3 kg/m2. Patients developed their symptoms (fever, cough, dyspnea, and fatigue) 1, 2, 4, and 14 days after surgery. One patient had unnoticed anosmia 2 days before surgery. Three patients were readmitted in hospital. All 4 patients were treated with hydroxychloroquine. In two patients who required admission in intensive care unit, other off-label therapies including antiretroviral and immunosuppressive agents were also administered. All patients survived. In conclusion, COVID-19 can complicate the postoperative course of patients after bariatric surgery. Correct diagnosis and management in the postoperative setting would be challenging. Timing of infection after surgery in our series would raise the possibility of hospital transmission of COVID-19: from asymptomatic patients at the time of bariatric surgery to the healthcare workers versus acquiring the COVID-19 infection by non-infected patients in the perioperative period.

Hydrogels are among the drug delivery systems that are used to modify drug release by the oral route. Inclusion of porous nanoparticles and cellulose nanofibers (CNF) in a hydrogel matrix structure improves the mechanical strength of the hydrogel and modifies drug release. CNF have been widely used for the preparation of biomedical systems because of low toxicity, biodegradability, and biocompatibility. Besides, a positive influence on mechanical and physical resistance is shown. In this study, nanocomposite hydrogels containing polyethylene glycol, Acrylamide, N, N′-methylene bis-acrylamide, and CNF are formulated, and then tetracycline was loaded into the hydrogels. Tetracycline release was measured using UV spectrometer. Morphology and microscopic structure of synthesized nanocomposites are studied using FE-SEM, XRD, and FTIR analyses. Moreover, the antibacterial activity of tetracycline nanocomposite hydrogels against Staphylococcus aureus and Escherichia coli was tested. Nanocomposite hydrogel oral toxicity test was performed in adult male Wistar rats. The results showed that the formulation has no significant statistical effect on the behavioral pattern, body weight, and clinical parameters of the experimental animals. Furthermore, pathological examination showed the normal structure of stomach and intestine. Antibacterial activity study showed that Staphylococcus aureus and E. Coli are sensitive to the formulated compound 3. Therefore, these formulations can be considered for future as oral drug delivery systems.

Introduction Identifying the potential effective factors of rhabdomyolysis-induced acute kidney injury (AKI) is of major importance for both treatment and logistic concerns. The present study aimed to evaluate the value of creatine kinase (CK) in predicting the risk of rhabdomyolysis-induced AKI through meta-analysis. Methods Two reviewers searched the electronic databases of Medline, EMBASE, Cochrane library, Scopus, and Google Scholar. Data regarding study design, patient characteristics, number of cases, mean and screening characteristics of CK, and final patient outcome were extracted from relevant studies. Pooled measures of standardized mean difference, OR, and diagnostic accuracy were calculated using STATA version 11.0. Result 5997 non-redundant studies were found (143 potentially relevant). 27 articles met the inclusion criteria but 9 were excluded due to lack of data. The correlation between serum CK and AKI occurrence was stronger in traumatic cases (SMD = 1.34, 95 % CI = 1.25–1.42, I 2  = 94 %; p  < 0.001). This correlation was more prominent in crush-induced AKI (adjusted OR = 14.7, 95 % CI = 7.63–28.52, I 2  = 0.0 %; p  = 0.001). Area under the ROC curve of CK in predicting AKI occurrence was 0.75 (95 % CI = 0.71–0.79). Conclusion The results of this meta-analysis declared the significant role of rhabdomyolysis etiology (traumatic/non-traumatic) in predictive performance of CK. There was a significant correlation between mean CK level and risk of crush-induced AKI. The pooled OR of CK was considerable, but its screening performance characteristics were not desirable.

Background Clarifying the dimensions and characteristics of obstetric telephone triage is important in improving the quality of services in the health system because researchers can evaluate the effectiveness of treatment, care and diagnostic measures in the form of obstetric telephone triage by developing a guideline. Therefore, this study aimed to design an Obstetric Telephone Triage Guideline (OTTG) using a mixed-method study. Methods The present study was carried out using an exploratory sequential mixed method study in two qualitative and quantitative phases. An inductive-deductive approach was also used to determine the concept of obstetric telephone triage. In this respect, a qualitative study and a literature review were used in the inductive and deductive stages, respectively. Moreover, the validity of the developed guideline was confirmed based on experts’ opinions and results of the AGREE II tool. Results The guideline included the items for evaluating the severity of obstetric symptoms at five levels including “critical”, “urgent”, “less urgent”, “no urgent”, and “recommendations”. The validity of the guideline was approved at 96%, 95%, 97%, 95%, 93%, and 100% for six dimensions of AGREE II including scope and purpose, stakeholder involvement, the rigor of development, clarity of presentation, applicability, and editorial independence, respectively. Conclusion The OTTG is a clinically comprehensive, easy-to-use, practical, and valid tool. This guideline is a standardized tool for evaluating the severity of symptoms and determining the urgency for obstetrics triage services. By using this integrated and uniform guideline, personal biases can be avoided, leading to improved performance and ensuring that patients are not overlooked. Additionally, the use of OTTG promotes independent decision-making and reduces errors in triage decision-making.

BackgroundThe present study aimed to assess dietary total antioxidant capacity (TAC), dietary phytochemical intake (PI), and dietary inflammatory index (DII) in patients with morbid obesity who are candidates of bariatric surgery and their association with anthropometric and biochemical parameters.Methods and MaterialsOne hundred seventy patients with morbid obesity who were referred to surgery clinic of Firoozgar Hospital were enrolled in the study. Ideal body weight and adjusted ideal body weight were calculated. The dietary data were collected using a food frequency questionnaire. Anthropometrics and biochemical parameters were assessed. A p-value of <0.05 was considered significant.ResultsThe strongest correlations of DII with dietary intakes and anthropometric and biochemical biomarkers were found for iron (p<0.0001). Significant association was also observed for ferritin (p=0.02) and transferrin (p=0.02). In terms of PI, The strongest associations were also found for iron (p<0.0001). Additionally, the value of body mass index (BMI) showed significant correlation with PI (p=0.04). The correlations of dietary total antioxidant indices with dietary intakes and anthropometric and biochemical biomarkers were assessed. Non-significant correlation was found between fasting blood sugar (FBS), hemoglobin A1C (HbA1C), vitamin B12, and vitamin D3 with ORAC index. Significant strong correlation showed for the value of iron in both ferric reducing ability of plasma (FRAP) and Oxygen Radical Absorbance Capacity (ORAC) indices (p<0.0001).ConclusionWe find statistical significance correlation for dietary PI and BMI. The inflammatory and antioxidant properties of diet were not related to biochemical markers associated with obesity.

Objective The objective of this systematic review and meta-analysis was to assess the value of uric acid in predicting acute kidney injury caused by traumatic rhabdomyolysis. Methods The search was conducted in MEDLINE, Scopus, Embase and Web of Science until November 1, 2023. Based on the inclusion and exclusion criteria, the articles were included by two independent researchers. Data regarding study design, patient characteristics, number of patients with and without AKI, mean and SD of uric acid and prognostic characteristics of uric acid were extracted from relevant studies. STATA version 17.0 was used to compute pooled measures of standardized mean differences, odds ratios, and diagnostic accuracy. I2 and chi-square tests were used to assess heterogeneity between studies. Results We found 689 non-redundant studies, 44 of them were potentially relevant. Six articles met the inclusion criteria and were included in the review. The results of the meta-analysis confirmed that there was a significant correlation between serum uric acid levels and the occurrence of AKI (SMD = 1.61, 95% CI = 0.69 to 2.54, I2 = 96.94%; p value = 0.001). There were no significant publication biases. Conclusion According to this meta-analysis, uric acid levels could be considered as a predictor of acute kidney injury following traumatic rhabdomyolysis.

Human intelligence relies on the vast number of neurons and their interconnections that form a parallel computing engine. If we tend to design a brain-like machine, we will have no choice but to employ many spiking neurons, each one has a large number of synapses. Such a neuronal network is not only compute-intensive but also memory-intensive. The performance and the configurability of the modern FPGAs make them suitable hardware solutions to deal with these challenges. This paper presents a scalable architecture to simulate a randomly connected network of Hodgkin-Huxley neurons. To demonstrate that our architecture eliminates the need to use a high-end device, we employ the XC7A200T, a member of the mid-range Xilinx Artix®-7 family, as our target device. A set of techniques are proposed to reduce the memory usage and computational requirements. Here we introduce a multi-core architecture in which each core can update the states of a group of neurons stored in its corresponding memory bank. The proposed system uses a novel method to generate the connectivity vectors on the fly instead of storing them in a huge memory. This technique is based on a cyclic permutation of a single prestored connectivity vector per core. Moreover, to reduce both the resource usage and the computational latency even more, a novel approximate two-level counter is introduced to count the number of the spikes at the synapse for the sparse network. The first level is a low cost saturated counter implemented on FPGA lookup tables that reduces the number of inputs to the second level exact adder tree. It, therefore, results in much lower hardware cost for the counter circuit. These techniques along with pipelining make it possible to have a high-performance, scalable architecture, which could be configured for either a real-time simulation of up to 5120 neurons or a large-scale simulation of up to 65536 neurons in an appropriate execution time on a cost-optimized FPGA.