Evaluation of cerebral microcirculation in a mouse model of systemic inflammation.

Significance: Perturbations in the microcirculatory system have been observed in neurological conditions, such as Alzheimer's disease or systemic inflammation. However, changes occurring at the level of the capillary are difficult to translate to biomarkers that could be measured macroscopically. Aim: We aim to evaluate whether transit time changes reflect capillary stalling and to what degree. Approach: We employ a combined spectral optical coherence tomography (OCT) and fluorescence optical imaging (FOI) system to investigate the relation between capillary stalling and transit time in a mouse model of systemic inflammation induced by intraperitoneal injection of lipopolysaccharide. Angiograms are obtained using OCT, and fluorescence signal images are acquired by the FOI system upon intravenous injection of fluorescein isothiocyanate via a catheter inserted into the tail vein. Results: Our findings reveal that lipopolysaccharide (LPS) administration significantly increases both the percentage and duration of capillary stalling compared to mice receiving a 0.9% saline injection. Moreover, LPS-induced mice exhibit significantly prolonged arteriovenous transit time compared to control mice. Conclusions: These observations suggest that capillary stalling, induced by inflammation, modulates cerebral mean transit time, a measure that has translational potential.

Rapid and sustained antidepressant effects of intravenous ketamine in treatment-resistant major depressive disorder and suicidal ideation: a randomized clinical trial.

BACKGROUND: Major depressive disorder (MDD) is the most disabling and burdensome mental disorder, negatively affecting an individual's quality of life and daily functioning. the current study was conducted with the aim of investigating the clinical effects of intravenous ketamine on symptoms of MDD and suicidal ideation. METHODS: The current randomized clinical trial was carried out on 64 patients diagnosed with treatment-resistant major depressive disorder between April and August 2022. The participants were randomly assigned to two groups: the intervention group received a dose of 0.5 mg/kg of ketamine, while the control group received normal saline. The Montgomery-Asberg Depression Scale and Beck's Suicidal Ideation Scale were utilized to assess depression and suicidal ideation, respectively. RESULTS: One hour after the administration of ketamine treatment, there was a notable and significant improvement in both depression symptoms (35.16 ± 8.13 vs. 14.90 ± 10.09) and suicidal ideation (6.74 ± 6.67 vs. 0.42 ± 1.52). Moreover, there were statistically significant differences in depression scores between the two groups at one hour, four hours, one day, three days, one week, one month, and two months after the administration of ketamine (p-value < 0.001). However, ketamine recipients frequently experienced side effects such as increased heart rate, headache, dizziness, and dissociative syndrome symptoms. CONCLUSION: The observed rapid onset of action and sustained effect demonstrate the potential of ketamine to provide relief from depressive symptoms in a shorter timeframe compared to traditional treatment approaches. These findings contribute to the growing body of evidence supporting the use of ketamine as a valuable therapeutic option for patients with treatment-resistant depression. IRCT REGISTRATION: IRCT registration number: IRCT20210806052096N1; IRCT URL: https://www.irct.ir/trial/62243 ; Ethical code: IR.ZUMS.REC.1400.150; Registration date: 2022-04-09.

Optimization of the Synthesis and Conjugation of the Methyl Rhamnan Tip of Pseudomonas aeruginosa A-Band Polysaccharide and Immunogenicity Evaluation for the Continued Development of a Potential Glycoconjugate Vaccine.

Pseudomonas aeruginosa is an antimicrobial-resistant bacterium that has no vaccine approved for human use. Additionally, it has been identified by the World Health Organization as a priority pathogen for novel vaccines and therapeutic development. We previously developed a synthetic mimic of the A-band polysaccharide tip that showed promise in terms of immunogenicity for use as a glycoconjugate vaccine. In this current manuscript, we improve upon the previous work to continue the development of this glycoconjugate vaccine. Herein, we report a higher-yielding synthesis of mimics containing a handle and a spacer that improved conjugation efficiency, resulting in better carbohydrate-to-protein ratios and also good immunogenicity of these conjugates in mice and rabbits. The data suggested that perhaps only a tetrasaccharide was required to induce an immune response capable of recognizing whole cells of P. aeruginosa.

Impact of stalling events on microcirculatory hemodynamics in the aged brain.

OBJECTIVE: The role of cerebral microvasculature in cognitive dysfunction can be investigated by identifying the impact of blood flow on cortical tissue oxygenation. In this paper, the impact of capillary stalls on microcirculatory characteristics such as flow and hematocrit (Ht) in the cortical angioarchitecture is studied. METHODS: Using a deterministic mathematical model to simulate blood flow in a realistic mouse cortex, hemodynamics parameters, including pressure, flow, vessel diameter-adjustable hematocrit, and transit time are calculated as a function of stalling events. RESULTS: Using a non-linear plasma skimming model, it is observed that Ht increases in the penetrating arteries from the pial vessels as a function of cortical depth. The incidence of stalling on Ht distribution along the blood network vessels shows reduction of RBCs around the tissue near occlusion sites and decreased Ht concentration downstream from the blockage points. Moreover, upstream of the occlusion, there is a noticeable increase of the Ht, leading to larger flow resistance due to higher blood viscosity. We predicted marked changes in transit time behavior due to stalls which match trends observed in mice in vivo. CONCLUSIONS: These changes to blood cell quantity and quality may be implicated in the development of Alzheimer's disease and contribute to the course of the illness.

Vascular synthesis based on hemodynamic efficiency principle recapitulates measured cerebral circulation properties in the human brain.

Quantifying anatomical and hemodynamical properties of the brain vasculature in vivo is difficult due to limited spatiotemporal resolution neuroimaging, variability between subjects, and bias between acquisition techniques. This work introduces a metabolically inspired vascular synthesis algorithm for creating a digital representation of the cortical blood supply in humans. Spatial organization and segment resistances of a cortical vascular network were generated. Cortical folding and macroscale arterial and venous vessels were reconstructed from anatomical MRI and MR angiography. The remaining network, including ensembles representing the parenchymal capillary bed, were synthesized following a mechanistic principle based on hydrodynamic efficiency of the cortical blood supply. We evaluated the digital model by comparing its simulated values with in vivo healthy human brain measurements of macrovessel blood velocity from phase contrast MRI and capillary bed transit times and bolus arrival times from dynamic susceptibility contrast. We find that measured and simulated values reasonably agree and that relevant neuroimaging observables can be recapitulated in silico. This work provides a basis for describing and testing quantitative aspects of the cerebrovascular circulation that are not directly observable. Future applications of such digital brains include the investigation of the organ-wide effects of simulated vascular and metabolic pathologies.

Metaverse and Medical Diagnosis: A Blockchain-Based Digital Twinning Approach Based on MobileNetV2 Algorithm for Cervical Vertebral Maturation.

Advanced mathematical and deep learning (DL) algorithms have recently played a crucial role in diagnosing medical parameters and diseases. One of these areas that need to be more focused on is dentistry. This is why creating digital twins of dental issues in the metaverse is a practical and effective technique to benefit from the immersive characteristics of this technology and adapt the real world of dentistry to the virtual world. These technologies can create virtual facilities and environments for patients, physicians, and researchers to access a variety of medical services. Experiencing an immersive interaction between doctors and patients can be another considerable advantage of these technologies, which can dramatically improve the efficiency of the healthcare system. In addition, offering these amenities through a blockchain system enhances reliability, safety, openness, and the ability to trace data exchange. It also brings about cost savings through improved efficiencies. In this paper, a digital twin of cervical vertebral maturation (CVM), which is a critical factor in a wide range of dental surgery, within a blockchain-based metaverse platform is designed and implemented. A DL method has been used to create an automated diagnosis process for the upcoming CVM images in the proposed platform. This method includes MobileNetV2, a mobile architecture that improves the performance of mobile models in multiple tasks and benchmarks. The proposed technique of digital twinning is simple, fast, and suitable for physicians and medical specialists, as well as for adapting to the Internet of Medical Things (IoMT) due to its low latency and computing costs. One of the important contributions of the current study is to use of DL-based computer vision as a real-time measurement method so that the proposed digital twin does not require additional sensors. Furthermore, a comprehensive conceptual framework for creating digital twins of CVM based on MobileNetV2 within a blockchain ecosystem has been designed and implemented, showing the applicability and suitability of the introduced approach. The high performance of the proposed model on a collected small dataset demonstrates that low-cost deep learning can be used for diagnosis, anomaly detection, better design, and many more applications of the upcoming digital representations. In addition, this study shows how digital twins can be performed and developed for dental issues with the lowest hardware infrastructures, reducing the costs of diagnosis and treatment for patients.

A Super-Efficient GSM Triplexer for 5G-Enabled IoT in Sustainable Smart Grid Edge Computing and the Metaverse.

Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are insufficient to meet the demanding requirements of industrial applications. The unique capabilities of 5G, such as numerous connections, high reliability, low latency, and large bandwidth, make it an excellent choice for smart grid services. The 5G network industry will heavily rely on the Internet of Things (IoT) to progress, which will act as a catalyst for the development of the future smart grid. This comprehensive platform will not only include communication infrastructure for smart grid edge computing, but also Metaverse platforms. Therefore, optimizing the IoT is crucial to achieve a sustainable edge computing network. This paper presents the design, fabrication, and evaluation of a super-efficient GSM triplexer for 5G-enabled IoT in sustainable smart grid edge computing and the Metaverse. This component is intended to operate at 0.815/1.58/2.65 GHz for 5G applications. The physical layout of our triplexer is new, and it is presented for the first time in this work. The overall size of our triplexer is only 0.007 λg2, which is the smallest compared to the previous works. The proposed triplexer has very low insertion losses of 0.12 dB, 0.09 dB, and 0.42 dB at the first, second, and third channels, respectively. We achieved the minimum insertion losses compared to previous triplexers. Additionally, the common port return losses (RLs) were better than 26 dB at all channels.

An octonion-based nonlinear echo state network for speech emotion recognition in Metaverse.

While the Metaverse is becoming a popular trend and drawing much attention from academia, society, and businesses, processing cores used in its infrastructures need to be improved, particularly in terms of signal processing and pattern recognition. Accordingly, the speech emotion recognition (SER) method plays a crucial role in creating the Metaverse platforms more usable​ and enjoyable for its users. However, existing SER methods continue to be plagued by two significant problems in the online environment. The shortage of adequate engagement and customization between avatars and users is recognized as the first issue and the second problem is related to the complexity of SER problems in the Metaverse as we face people and their digital twins or avatars. This is why developing efficient machine learning (ML) techniques specified for hypercomplex signal processing is essential to enhance the impressiveness and tangibility of the Metaverse platforms. As a solution, echo state networks (ESNs), which are an ML powerful tool for SER, can be an appropriate technique to enhance the Metaverse's foundations in this area. Nevertheless, ESNs have some technical issues restricting them from a precise and reliable analysis, especially in the aspect of high-dimensional data. The most significant limitation of these networks is the high memory consumption caused by their reservoir structure in face of high-dimensional signals. To solve all problems associated with ESNs and their application in the Metaverse, we have come up with a novel structure for ESNs empowered by octonion algebra called NO2GESNet. Octonion numbers have eight dimensions, compactly display high-dimensional data, and improve the network precision and performance in comparison to conventional ESNs. The proposed network also solves the weaknesses of the ESNs in the presentation of the higher-order statistics to the output layer by equipping it with a multidimensional bilinear filter. Three comprehensive scenarios to use the proposed network in the Metaverse have been designed and analyzed, not only do they show the accuracy and performance of the proposed approach, but also the ways how SER can be employed in the Metaverse platforms.

Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer.

Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters.

Modeling the spatial variation of calcium carbonate equivalent to depth using machine learning techniques.

Inorganic carbon is the largest source of carbon in terrestrial surface, particularly in arid and semiarid regions, including the Chahardowli Plain in western Iran. Inorganic carbon plays an equal or greater role than organic soil carbon in these areas, although less attention has been paid in quantifying their variability. The objective of this study was to model and map calcium carbonate equivalent (CCE) presenting inorganic carbon in soil using machine learning and digital soil mapping techniques. Chahardowli Plain in foothills of the Zagros Mountains in the southeast of Kurdistan Province in Iran was taken as a case study area. CCE was measured at 0-5, 5-15, 15-30, 30-60, and 60-100 cm depths following GloalSoilMap.net project specifications. A total of 145 samples were collected from 30 soil profiles using the conditional Latin hypercube (cLHS) method of sampling. Relationships between CCE and environmental predictors were modeled using random forest (RF) and decision tree (DT) models. In general, the RF model performed slightly superior than the DT model. The mean value of CCE increased with soil depth, from 3.5% (0-5 cm) to 63.8% (30-60 cm). Remote sensing (RS) variables and terrestrial variables were equally important. The importance of RS variables was higher at the surface than terrestrial variables, and vice versa. The most significant variables were Channel Network Base Level (CNBL) variable and Difference Vegetation Index (DVI) with the same variable importance value (21.1%). In areas affected by river activities, the use of the CNBL and vertical distance to channel networks (VDCN) as variables in digital soil mapping (DSM) could increase the accuracy of soil property prediction maps. The VDCN played a principal role in soil distribution in the study area by affecting the rate of discharge and, thus, erosion and sedimentation. A high percentage of carbonate in parts of the region could exacerbate nutrient deficiencies for most crops and provide information for sustainably managing agricultural activity.

Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance.

PURPOSE: Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation. To simplify the gruelling, subjective, and prone-to-error task of interpreting WBS scans, we developed deep learning (DL) models to automate two major analyses, namely (i) classification of scans into normal and abnormal and (ii) discrimination between malignant and non-neoplastic bone diseases, and compared their performance with human observers. MATERIALS AND METHODS: After applying our exclusion criteria on 7188 patients from three different centers, 3772 and 2248 patients were enrolled for the first and second analyses, respectively. Data were split into two parts, including training and testing, while a fraction of training data were considered for validation. Ten different CNN models were applied to single- and dual-view input (posterior and anterior views) modes to find the optimal model for each analysis. In addition, three different methods, including squeeze-and-excitation (SE), spatial pyramid pooling (SPP), and attention-augmented (AA), were used to aggregate the features for dual-view input models. Model performance was reported through area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity and was compared with the DeLong test applied to ROC curves. The test dataset was evaluated by three nuclear medicine physicians (NMPs) with different levels of experience to compare the performance of AI and human observers. RESULTS: DenseNet121_AA (DensNet121, with dual-view input aggregated by AA) and InceptionResNetV2_SPP achieved the highest performance (AUC = 0.72) for the first and second analyses, respectively. Moreover, on average, in the first analysis, Inception V3 and InceptionResNetV2 CNN models and dual-view input with AA aggregating method had superior performance. In addition, in the second analysis, DenseNet121 and InceptionResNetV2 as CNN methods and dual-view input with AA aggregating method achieved the best results. Conversely, the performance of AI models was significantly higher than human observers for the first analysis, whereas their performance was comparable in the second analysis, although the AI model assessed the scans in a drastically lower time. CONCLUSION: Using the models designed in this study, a positive step can be taken toward improving and optimizing WBS interpretation. By training DL models with larger and more diverse cohorts, AI could potentially be used to assist physicians in the assessment of WBS images.

Assessment of macronutrients status using digital soil mapping techniques: a case study in Maru'ak area in Lorestan Province, Iran.

The present study was conducted to compare generalized linear model (GLM), random forest (RF), and Cubist to produce available phosphorus (AP) and potassium (AK) maps and to identify the covariates that control mineral distribution in Lorestan Province, Iran. To this end, the locations for collecting 173 soil samples were determined through the conditioned Latin hypercube sampling (cLHS) method, at four different land-uses (orchards, paddy fields, agricultural, and abandoned fields). The performance of the models was assessed by coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) indices. The results showed that the RF model fitted better than GLM and Cubist models and could explain 40 and 57% of AP and AK distribution, respectively. The R2, RMSE, and MAE of the RF model were 0.4, 2.81, and 2.43 for predicting AP and equal to 0.57, 143.77, and 116.61 for predicting AK, respectively. The most important predictors selected by the RF model were valley depth and soil-adjusted vegetation index (SAVI) for AP and AK, respectively. The maps showed higher AP and AK content in apricot orchards compared to other land-uses. No difference was observed between AP and AK content on paddy fields, agricultural, and abandoned areas. The higher AP and AK contents were related to orchard management practices, such as failure to dispose of plant residuals and fertilizer consumption. It can be concluded that the orchards (by increasing soil quality) was the best land-use in line with sustainable management for the study area. However, generalizing the results needs more detailed research.

A multiplex inhalation platform to model in situ like aerosol delivery in a breathing lung-on-chip.

Prolonged exposure to environmental respirable toxicants can lead to the development and worsening of severe respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD) and fibrosis. The limited number of FDA-approved inhaled drugs for these serious lung conditions has led to a shift from in vivo towards the use of alternative in vitro human-relevant models to better predict the toxicity of inhaled particles in preclinical research. While there are several inhalation exposure models for the upper airways, the fragile and dynamic nature of the alveolar microenvironment has limited the development of reproducible exposure models for the distal lung. Here, we present a mechanistic approach using a new generation of exposure systems, the Cloud α AX12. This novel in vitro inhalation tool consists of a cloud-based exposure chamber (VITROCELL) that integrates the breathing AXLung-on-chip system (AlveoliX). The ultrathin and porous membrane of the AX12 plate was used to create a complex multicellular model that enables key physiological culture conditions: the air-liquid interface (ALI) and the three-dimensional cyclic stretch (CS). Human-relevant cellular models were established for a) the distal alveolar-capillary interface using primary cell-derived immortalized alveolar epithelial cells (AXiAECs), macrophages (THP-1) and endothelial (HLMVEC) cells, and b) the upper-airways using Calu3 cells. Primary human alveolar epithelial cells (AXhAEpCs) were used to validate the toxicity results obtained from the immortalized cell lines. To mimic in vivo relevant aerosol exposures with the Cloud α AX12, three different models were established using: a) titanium dioxide (TiO2) and zinc oxide nanoparticles b) polyhexamethylene guanidine a toxic chemical and c) an anti-inflammatory inhaled corticosteroid, fluticasone propionate (FL). Our results suggest an important synergistic effect on the air-blood barrier sensitivity, cytotoxicity and inflammation, when air-liquid interface and cyclic stretch culture conditions are combined. To the best of our knowledge, this is the first time that an in vitro inhalation exposure system for the distal lung has been described with a breathing lung-on-chip technology. The Cloud α AX12 model thus represents a state-of-the-art pre-clinical tool to study inhalation toxicity risks, drug safety and efficacy.

Digital Twin Model of Electric Drives Empowered by EKF.

Digital twins, a product of new-generation information technology development, allows the physical world to be transformed into a virtual digital space and provide technical support for creating a Metaverse. A key factor in the success of Industry 4.0, the fourth industrial revolution, is the integration of cyber-physical systems into machinery to enable connectivity. The digital twin is a promising solution for addressing the challenges of digitally implementing models and smart manufacturing, as it has been successfully applied for many different infrastructures. Using a digital twin for future electric drive applications can help analyze the interaction and effects between the fast-switching inverter and the electric machine, as well as the system's overall behavior. In this respect, this paper proposes using an Extended Kalman Filter (EKF) digital twin model to accurately estimate the states of a speed sensorless rotor field-oriented controlled induction motor (IM) drive. The accuracy of the state estimation using the EKF depends heavily on the input voltages, which are typically supplied by the inverter. In contrast to previous research that used a low-precision ideal inverter model, this study employs a high-performance EKF observer based on a practical model of the inverter that takes into account the dead-time effects and voltage drops of switching devices. To demonstrate the effectiveness of the EKF digital twinning on the IM drive system, simulations were run using the MATLAB/Simulink software (R2022a), and results are compared with a set of actual data coming from a 4 kW three-phase IM as a physical entity.

Patient effective dose and radiation biological risk in the chest and abdominopelvic computed tomography.

The incidence and mortality (per 100,000) rates in chest CT are highest for the lungs and breasts (incidence: lung = 116, breast = 98.64; mortality: lung = 113.43, breast = 49.72). Abdominopelvic CT scans showed the highest incidence for stomach (79.57), colon (62.86), bladder (48.69), and liver (28.63), respectively. Mortality is highest for the bladder (80.44), stomach (72.43), colon (69.02), and liver (63.78), respectively. This study helps to better understand the concept of radiation dose and the numbers reported as organ dose and effective dose and identify the probability of the stochastic effect.

Long non-coding RNA signatures and related signaling pathway in T-cell acute lymphoblastic leukemia.

T-cell acute lymphoblastic leukemia (T-ALL) is a malignancy caused by clonal proliferation of T-cell pre-cursors arising from the thymus. Although the optimized chemotherapy regimen could improve the outcome of such patients, some challenges such as higher risk for induction failure, early relapse and isolated central nervous system (CNS) relapse occurring in T-ALL patients are of great significance, leading to increased mortality rates. Long non-coding RNA (lncRNA) is a key component involved in cell signaling through a variety of mechanisms in regulating gene expression. Oncogenes and tumor suppressors are no exception and their expression can be affected by lncRNAs. In addition, accumulating researches in samples from T-ALL patients as well as pre-clinical studies in mice suggest that the expression profile of lncRNAs in T-ALL could be aberrant, resulting in deregulation of target genes and downstream signaling pathways. In addition, accumulating researches in samples from T-ALL patients as well as pre-clinical studies in mice suggest that the expression profile of lncRNAs in T-ALL could be aberrant, resulting in deregulation of target genes and downstream signaling pathways. These lncRNAs may be determinants of proliferation, apoptosis, and drug resistance observed in T-ALL. Thus, lncRNAs can be a good tool to develop novel strategies against cancer cells in the treatment of relapsed and refractory T-ALL. They can also act as promoting biomarkers in assessing T-ALL and differentiating between patients with poor prognosis and good prognosis.

Structural Characterization and Evaluation of an Epitope at the Tip of the A-Band Rhamnan Polysaccharide of Pseudomonas aeruginosa.

Pseudomonas aeruginosa produces a variety of cell surface glycans. Previous studies identified a common polysaccharide (PS) antigen often termed A-band PS that was composed of a neutral d-rhamnan trisaccharide repeating unit as a relatively conserved cell surface carbohydrate. However, nuclear magnetic resonance (NMR) spectra and chemical analysis of A-PS preparations showed the presence of several additional components. Here, we report the characterization of the carbohydrate component responsible for these signals. The carbohydrate antigen consists of an immunogenic methylated rhamnan oligosaccharide at the nonreducing end of the A-band PS. Initial studies performed with the isolated antigen permitted the production of conjugates that were used to immunize mice and rabbits and generate monoclonal and polyclonal antibodies. The polyclonal antibodies were able to recognize the majority of P. aeruginosa strains in our collection, and three monoclonal antibodies were generated, one of which was able to recognize and facilitate opsonophagocytic killing of a majority of P. aeruginosa strains. This monoclonal antibody was able to recognize all P. aeruginosa strains in our collection that includes clinical and serotype strains. Synthetic oligosaccharides (mono- to pentasaccharides) representing the terminal 3-O-methyl d-rhamnan were prepared, and the trisaccharide was identified as the antigenic determinant required to effectively mimic the natural antigen recognized by the broadly cross-reactive monoclonal antibody. These data suggest that there is considerable promise in this antigen as a vaccine or therapeutic target.

Synthesis and Immunogenicity of a Methyl Rhamnan Pentasaccharide Conjugate from Pseudomonas aeruginosa A-Band Polysaccharide.

Pseudomonas aeruginosa was added to the World Health Organization's priority pathogen list for research and development of new antibiotics in 2017. Alongside the development of new antibiotics to fight antimicrobial-resistant P. aeruginosa, vaccines would be an appealing addition to the toolbox health professionals have against this bacteria, which causes life-threatening respiratory infections. Recently, the structure of a novel immunogenic terminal carbohydrate moiety on the cell surface of P. aeruginosa was elucidated, consisting of a 3-O-methyl (1→4)-α-d-rhamnan pentasaccharide. As isolating this oligosaccharide from P. aeruginosa in sufficient amounts for producing a conjugate vaccine is challenging, herein we describe the synthesis of 3-O-methyl d-rhamnose oligosaccharide. We also report the conjugation of the synthetic pentasaccharide to human serum albumin and its resulting immunogenicity.

Biofeedback, biofeedback plus fiber laxative, and biofeedback plus osmotic laxative regimens to treat constipation in patients with pelvic floor dyssynergia: A randomized controlled trial.

BACKGROUND: Pelvic floor dyssynergia (PFD) is one of the causes of chronic constipation. Laxative-based therapies are not very effective in treating this type of constipation. The therapeutic effectiveness of three therapeutic strategies, including biofeedback (BOF) alone, BOF+ fiber laxative (psyllium), and BOF +osmotic laxative (polyethylene glycol; [PEG]), was assessed in patients with constipation secondary to PFD. METHODS: Eighty-eight patients with constipation were included during a period from 2017 to 2018. Thirty-two patients were treated with BOF alone, 25 patients received BOF+ fiber laxative (psyllium), and 31 patients received BOF+ osmotic laxative (PEG) for 2 to 3 months. A checklist was used to compare outcomes before and after the interventions. RESULTS: Satisfaction rates from the treatments were 60.83%, 46.88%, and 41.32% in the BOF, BOF + psyllium, and BOF + PEG groups, respectively. Women had a higher satisfaction rate compared to men. Defecation quality improved, and the frequency of enema usage decreased (p <0.05) in all the groups after intervention. Difficulty in evacuation, need for digitation, and return to the toilet after defecation significantly improved in the BOF alone group. Using laxatives reduced straining during evacuation and increased the duration of defecation. All the three regimens reduced rectal bleeding (p <0.05). CONCLUSIONS: Combinations of laxatives with BOF did not offer significant therapeutic benefit. As laxatives may cause dissatisfaction and incomplete/prolonged defecations in patients with PFD, adding laxatives to the BOF regimen is not recommended for these patients.

The Adsorption and Degradation of 2, 4-D Affected by Soil Organic Carbon and Clay.

More has yet to be indicated on the adsorption and degradation processes, determining herbicides recycling in the environment. The sorption and degradation of 2, 4-D, affected by organic carbon (1.92-2.81%), soil clay (20-30%) and pH of the citrus orchards of Mazandaran province, Iran was investigated using HPLC equipped with UV detector for the identification and quantification of soil 2, 4-D. The adsorption (kd) and degradation (Kdeg) coefficients were determined using Freundlich and the first-degree kinetic equations. Gardens C (2.45 mL g-1), and B (0.3 mL g-1), with the highest (8.2 g day-1) and least (2.7 g day-1) degradation coefficients, had the highest and lowest Kd values. Kd variations with pH indicated higher adsorption of 2, 4-D in acidic pH. Due to the high presence of functional groups and soil biological activities, organic carbon affected the adsorption and degradation rates more effectively, which is of economic and environmental significance.