Objectives In a COVID-19 sero-surveillance cohort study with predominantly healthy and vaccinated individuals, the objectives were (i) to investigate longitudinally the factors associated with the quantitative dynamics of anti-spike IgG antibody levels, (ii) to evaluate whether the antibody levels were associated with protection from SARS-CoV-2 infection and (iii) to assess whether the association was different in the pre-Omicron compared with the Omicron period. Methods The QuantiVac Euroimmun ELISA test was used to quantify anti-S1 IgG levels. The entire study period (16 months), the 11-month pre-Omicron period and the cross-sectional analysis prior to the Omicron surge included 3219, 2310 and 895 reactive serum samples from 949, 919 and 895 study participants, respectively. Mixed-effect linear, mixed-effect time-to-event and logistic regression models were used to achieve the objectives. Results Age and time since infection or vaccination were the only factors associated with a decline of anti-S1 IgG levels. Antibody levels were significantly associated with protection from SARS-CoV-2 infection, and the association was higher for the Omicron than for the Alpha and Delta variants. In a prediction model, it was estimated that >8000 BAU/mL anti-S1 IgG was required to reduce the risk of infection with Omicron variants by 20% to 30% for 90 days. Conclusions Anti-S1 IgG antibody levels are associated with protection from infection. The levels in the pre-Omicron periods were less significant than during the Omicron surge, which in turn required very high levels for protection in a statistical model.

This work proposes a new method for estimating the excess Gibbs energy and activity of liquid alloy based on partial pair distribution function (PPDF). This work utilized the PPDF to calculate the parameters of the MIVM, RSM, Wilson, and NRTL model. This new method offers simplicity, numerical calculation stability, and excellent reproducibility. The work examined the impact of expressing the model parameters as first-order linear functions of the components or as constants on the accuracy of the estimation. When the model parameters were treated as constants, MIVM and RSM provided an average relative deviation (ARD) of activity of less than ±20% for 15 and 10 alloys. When model parameters were expressed as a function of components, QCM showed the best estimation performance, having 9 alloys with an ARD of less than ±20%.

This paper proposes a novel deep learning architecture named as Inverted Funnelized Multilayer Network (IFMN) for detecting intrusions in Internet of Things (IoT) networks. The proposed approach for intrusion detection employs a feature selection model that uses a Hybrid Yellow saddle goatfish algorithm and Particle Swarm Optimization algorithm for Feature Selection (HYFSPSO) to identify the optimal features. The effectiveness of the selected features is evaluated using a decision tree (DT) classification method, ensuring only the most informative features are used in the deep learning architecture for intrusion detection. For analyzing and proving the effectiveness of proposed scheme the current research has used three benchmark datasets i.e. KDD-CUP99, NSL-KDD and UNSW-NB15 datasets. The simulations of the proposed architecture are conducted in MATLAB and evaluated using performance matrices. While comparing the outcomes on 3 datasets results revealed that proposed HYSGPSO-DL based IDS approach is more effective on NSL-KDD and UNSW-NB15 datasets with an accuracy of 99.96% and 99.80%, while as it achieved an accuracy of 99.53% on KDD-CUP99 dataset. Additionally, comparative analysis with existing intrusion detection systems shows that proposed scheme outperforms the state-of-the-art methods.

This paper proposes for the first time the preparation of a series of amino acid ionic liquids (AAILs) via one-step hydrolysis of cheap lactams for the capture of CO2. The structures of the prepared AAILs are confirmed using NMR, FTIR, and ESIMS, and their physical properties are also determined. It is found that these AAILs are reversible CO2 absorbents with very high absorption capacities (0.15 to 0.18 g·g1 at 313.2 K and 1.0 bar), better than almost all task-specific ionic liquids reported in literatures. The absorption mechanism is also elucidated to be a combination of 1:1 and 2:1 stoichiometric reaction of AAILs with CO2 from NMR, FTIR, reaction equilibrium thermodynamical modelling (RETM) and quantum calculations. The AAILs have the advantages of simple synthesis, high yield, and using available cheap raw materials. It is believed that this kind of AAILs have great potentials to be used as efficient CO2 absorbents.

The present study though preliminary in nature but the generated baseline information will be helpful to design conservation planning for the endangered plant species Strychnos potatorum and its associated species as these diversified vegetation patches are connective corridors of flora and fuana. In addition, soil samples were collected from 0-20 cm depth and analyzed physically and chemically. Keywords: Strychnos potatorum , Soil analysis, Associated species, Conservation

A document by Chao Ren. Click on the document to view its contents.

The application of heterogeneous catalysts in dimethyl carbonate (DMC) synthesis from methanol is hindered by low activation efficiency of methanol to methoxy intermediates (CH3O*), which is the key intermediate for DMC generation. Herein, a catalyst of alkali metal K anchored on the CuO/ZnO oxide is rationally designed for offering Lewis acid-base pairs as dual active centers to improve the activation efficiency of methanol. Characterizations of CO2-TPD, NH3-TPD, XPS, and DRIFTS revealed that the addition of Lewis base K observably boosted the dissociation of methanol and combined with Lewis acid CuO/ZnO oxide to adsorb the formed CH3O* stably, thus synergistically promoted the transesterification. Finally, the CuO/ZnO-9%K2O catalyst exhibited the optimal catalytic activity, achieving a high yield of 74.4% with an excellent selectivity of 98.9% for DMC at a low temperature of 90 °C. The strategy of constructing Lewis acid-base pairs provides a reference for the design of heterogeneous catalysts.

Bladed mixers are widely used for processing granular materials where significant mechanical energy is required to produce the desired blend. Some mechanical energy is dissipated within the granular medium, generating heat during this process. However, our knowledge of the heat generation mechanisms without external thermal loads is still lacking. This study uses an overhead stirrer to mix granular materials and investigate heat generation by monitoring the temperature changes in the granular bed. Additionally, first-order kinetic equations are used to extrapolate the experimental data to a thermal equilibrium where the heat generation and heat loss rates are equal. Lead, steel, and glass particles are used under various operating conditions. It is observed that metallic particles heat up faster owing to their lower heat capacity. Also, increasing the rotation speed, fill ratio and particle size result in a greater temperature increase. Moreover, flat blades induce more heat generation compared to tilted blades.

There is presently a paucity of mass transfer studies to single nanocatalyst particles with diameters ranging from 1 nm to 100 nm. We have experimentally investigated the flux associated with the electrocatalytic reduction of hexacyanoferrate (III) to hexacyanoferrate (II) on gold nanospheres. We have found that the flux of hexacyanoferrate (III) to the surface is insensitive to particle sizes in the range of 30 nm ≤ dp ≤ 100 nm. For particle sizes of 5 nm ≤ dp ≤ 30 nm, we see the flux increase sharply as the particle size decreases. While qualitatively the same, the measured fluxes are one to three orders of magnitude smaller than that predicted by a diffusion-limited model. Factors in addition to diffusion are evaluated and discussed, including enhanced surface reactivity of nanoparticles, flux concentration due to sphericity, interactions among particles, presence of stabilizing layers on the particle, and advection due to Brownian motion.

Adaptive deep brain stimulation (aDBS) is a promising therapy for Parkinson's disease (PD) that has the potential to improve treatment outcomes and reduce side effects compared to traditional, continuous DBS. In aDBS, the stimulation parameters are adjusted in real-time based on feedback from the patient's brain signals or symptoms. This allows for more precise and personalized stimulation, tailored to the patient's needs and fluctuating symptoms. Several studies have demonstrated the feasibility and effectiveness of aDBS in PD patients, with improvements in motor symptoms, quality of life, and reduction in medication usage. Furthermore, aDBS may have the potential to reduce the incidence of DBS-related side effects, such as speech and gait disturbances, as well as decrease battery usage and prolong device longevity. Despite the promising results, aDBS is still in the early stages of development and requires further research to fully understand its mechanisms and optimize its parameters. Challenges remain in identifying the optimal biomarkers for aDBS, designing closed-loop systems that can effectively integrate feedback signals and stimulation parameters, and assessing long-term efficacy and safety. In conclusion, aDBS is a promising approach that has the potential to revolutionize DBS therapy for PD. Further research is needed to refine the technology, identify the optimal biomarkers, and determine the most effective stimulation strategies for individual patients.

Background- Premature births are on a rise. Mechanical ventilation (MV) is required by very low birth weight (VLBW) neonates with respiratory failure. Optimal extubation remains challenging, as approximately 30% of ventilated preterm infants fail extubation. The incidence and risk factors for extubation failure (EF) may vary from country to country. Methods- This prospective observational study was conducted among VLBW neonates to find the EF rate and risk factors. Results- Of the 60 enrolled neonates with a mean gestational age of 30.94±1.55 weeks and mean birth weight of 1198±201 grams, 25 (41.7%) failed extubation. The risk factors associated with EF were shock (0.002), use of vasopressor (0.005), presence of complications (pneumothorax, ventilator-associated pneumonia) (0.032), anemia (0.017), culture-positive sepsis (0.042), duration of MV (0.006), post-extubation FiO2 requirement (<0.001) and post-extubation blood gas parameters- pH (<0.001), PaCO2 (0.001), HCO3 (0.001), SPO2/FiO2 ratio (p<0.001). On multivariate regression, Silverman Anderson (SAS) Score ≥ 5 (OR 5.23; 95% CI 2.3-12; <0.001) and anemia (hemoglobin less than 12 gram/dl) (OR1.71;95%CI 0.96-3.06; 0.028) were significant risk factors. Significantly more babies in the EF group expired (0.002). Also, neonates who failed extubation had an increased length of NICU stay as compared to those who succeeded 27±14 vs 20±12 days (p=0.048). Conclusion- Forty-one percent of VLBW neonates failed extubation. Post extubation SAS of ≥ 5, anemia (hemoglobin less than 12 grams) were independent risk factors for EF. Neonates with EF had poor outcomes with more deaths and prolong NICU stay.

Background: Multiple Breath washout (MBW) represents an important tool to detect early a possible pulmonary exacerbation especially in Cystic Fibrosis (CF) disease. Lung clearance index (LCI) is the most commonly reported multiple breath washout (MBW) index and in the last years was used as management measure for evaluation. Our aim was to analyze clinical utility of LCI index variability in pulmonary exacerbation in CF. Methods: A single-center study was conducted at CF Unit of Bambino Gesù Children’s Hospital among hospitalized > 3 years patients for pulmonary exacerbations and treated with antibiotic intravenous treatment for 14 days. MBW and spirometry were evaluated within 72 hours of admission to hospital and at the end of hospitalization. Descriptive analysis was conducted and correlations between quantitative variables were investigated. Results: Fifty-seven patients (M22/F35) with an average age 18.56 (± 8.54) years were enrolled. LCI 2.5 was significantly reduced at the end of antibiotic treatment in both pediatric and adult populations with an average improvement of -6% in 37/57 patients. 26/57 are stable in LCI values. A significant elevation of +7.30% was found for FEV1%. 4/57 (7.02 %) had a significant deterioration in LCI values at the end of IV antibiotic treatment. A positive good correlation among LCI 2.5 and Scond (rho= +0.615, p=0.000) and LCI 2.5 and Sacin (rho=+0.649, p=0.000) and a negative strong correlation between FEV1% and LCI 2.5 were found in post treatment period. A better increase of LCI 2.5 in < 18 years (-10.27% vs -4.57%) than adult was noticed. Conclusions: LCI may have a role in the routine clinical care of CF patients as a good tool to assess response to intravenous antibiotic therapy.

Childhood interstitial lung disease (chILD) is a heterogeneous group of diffuse lung diseases (DLD) that can be challenging to diagnose. With relative rarity of individual entities, data are limited on disease prevalence, care patterns, and healthcare utilization. The objective of this study was to evaluate chILD prevalence and review diagnostic and clinical care patterns at our center. A single-center, retrospective cohort study was conducted of patients receiving care at the Children’s Hospital of Philadelphia (CHOP) between January 1, 2019, and December 31, 2021. Through query of selected ICD-10 billing codes relevant for chILD/DLD, a total of 306 patients were identified receiving care during this period. Respiratory symptom onset was documented to have developed before two years of age for 40% of cases. The most common diagnostic categories included those with oncologic disease (21.2%), bronchiolitis obliterans (10.1%), and connective tissue disease (9.5%). Genetic testing was performed in 49% of cases, while 36% underwent lung biopsy. Hospitalization at CHOP had occurred for 80.4% of patients, with 45.1% ever hospitalized in an intensive care unit. One-third of children had required chronic supplemental oxygen. Seven (2.3%) patients died during this three-year period. Collectively, these data demonstrate the scope of chILD and extent of health care utilization at a large volume tertiary care center. This approach to cohort identification and EMR-driven data collection in chILD provides new opportunities for cohort analysis and will inform the feasibility of future studies.

Childhood interstitial and diffuse lung disease (chILD) encompasses a broad spectrum of rare disorders. The Children’s Interstitial and Diffuse Lung Disease Research Network (chILDRN) established a prospective registry to advance knowledge regarding etiology, phenotype, natural history, and management of these disorders. This longitudinal, observational, multicenter registry utilizes single-IRB reliance agreements, with participation from 25 chILDRN centers across the U.S. Clinical data are collected and managed using the Research Electronic Data Capture (REDCap) electronic data platform. We report the study design and some elements of the initial Registry enrollment cohort, which includes 683 subjects with a broad range of chILD diagnoses. The most common diagnosis reported was neuroendocrine cell hyperplasia of infancy (NEHI), with 155 (23%) subjects. Components of underlying disease biology were identified by enrolling sites, with cohorts of interstitial fibrosis, immune dysregulation, and airway disease being most commonly reported. Prominent morbidities affecting enrolled children included home supplemental oxygen use (63%) and failure to thrive (46%). This Registry is the largest longitudinal chILD cohort in the U.S. to date, providing a powerful framework for collaborating centers committed to improving the understanding and treatment of these rare disorders.

AIMS The aim of this umbrella review was to identify tools and guidelines available to aid the deprescribing process of potentially inappropriate medications (PIM), evaluate their methods of development and validation, and describe at what level of evidence medications are included in the tools and guidelines. METHODS Searches were conducted on MEDLINE(Ovid), Embase.com, Cochrane CDSR, CINAHL (EBSCO), Web of Science Core Collection, and guideline databases from the date of inception to July 7, 2022. We described and compared the different characteristics and contents of tools and guidelines. RESULTS From 10,606 records screened, 23 systematic reviews and guidelines met the inclusion criteria. We then identified 90 tools (67 explicit, 12 mixed, 11 implicit), and 9 guidelines. The majority were developed for use in older adults (n = 74). Nineteen tools targeted older adults with limited life expectancy (LLE), and 7 targeted children <18 years. The majority (97%) of explicit/mixed tools were validated through Delphi techniques (n = 61). Two tools and eight guidelines provided information on the level of evidence for the included medications, of which less than a quarter were included based on high-quality evidence. CONCLUSION Existing tools are available for a diversity of populations. Discrepancies identified in categorizing medications as appropriate or not can be a result of low quality evidence. Particularly, tools for patients with LLE were developed based on very limited evidence, and research to generate this evidence is highly needed. Our medication lists, along with the level of evidence, could facilitate efforts to strengthen the evidence.

Dear Editor, I am writing to submit a letter titled “Usage of a Novel Inhaler Device for the Management of Asthma in Children with Special Needs” for your consideration. This letter highlights the importance of the inhaler device technique in children with asthma when they have comorbid conditions such as attention-deficit/hyperactivity disorder (ADHD) and/or autism spectrum disorder (ASD). These comorbidities often make it challenging for physicians to plan a treatment regimen, as it impacts the patient’s adherence to medication. We evaluated the effectiveness of a breath-actuated inhaler (BAI) in managing poorly controlled asthma in three pediatric patients with special needs. All patients showed significant improvement in their asthma symptoms, were well-controlled after one month of using the BAI and they found that device was easy to use. This letter contributes to the growing body of evidence supporting the effectiveness of BAI in the management of asthma, particularly in children with special needs. We hope this work to be submitted under the letter to editor section and it will be of interest to your readership and contribute to improving asthma management in children with comorbidities. Thank you for your consideration.

Everolimus is an inhibitor of mammalian target of rapamycin (mTOR) widely used as an immunosuppressant in transplant patients. In transplantation setting, several recommendations for therapeutic drug monitoring are available, due to potential drug-drug interactions with chronic medication, which can affect everolimus pharmacokinetic profile. Everolimus is also used in breast, neuroendocrine and renal cancer, at higher doses than in transplantation and without systematic drug monitoring requirement. We present a case-report of a 72years-old woman with epilepsy history to whom everolimus 10mg/daily was prescribed as third line of treatment for renal cell carcinoma. The patient´s chronic medication was checked by the outpatient hospital pharmacist before initiating treatment. As a result, two major interactions with inducers of CYP3A4 metabolism as carbamazepine and phenytoin were detected. Since trough plasma concentration of everolimus could be affected, by these pharmacokinetic interactions,a therapeutic drug monitoring of everolimus was proposed. Based on the literature, Cmin plasma concentration (Cminss) of everolimus over 10ng/ml is associated with better response to treatment and progression free survival. During the oncologist follow-up visit and according to pharmaceutical recommendations, everolimus dosage was increased to 10mg every 12 hours. In the following determinations of trough plasma everolimus concentration, Cmin grow up from 3.7ng/ml to 10.8ng/mL. Depending on the CYP3A4 induction capacity and potency of the drug/s administered concomitant to everolimus and how many of them can potentially interact with it, therapeutic everolimus monitoring would be advisable in order to adjust dosage if required to prevent underexposure.

Somkiat Seesanong1, Chaowared Seangarun2, Banjong Boonchom2,3,4*, Chuchai Sronsri2 , Nongnuch Laohavisuti5, Nattaya Montri1, Somphob Thompho6, Wimonmat Boonmee7, Pesak Rungrojchaipon4

A document by Mohsin Ejaz. Click on the document to view its contents.

Population genomics applied to game species conservation can help delineate management units, ensure appropriate harvest levels, and identify populations needing genetic rescue to safeguard their adaptive potential. The ruffed grouse (Bonasa umbellus) is rapidly declining in much of the eastern USA due to a combination of forest maturation and habitat fragmentation. More recently, mortality from West Nile Virus may have affected connectivity of local populations; however, genetic approaches have never explicitly investigated this issue. In this study, we sequenced 54 individual low-coverage (~5X) grouse genomes to characterize population structure and assess migration rates across the landscape to identify potential barriers to gene flow. Surprisingly, we identified two genomic clusters with no clear geographic correlation, with large blocks of genomic differentiation associated with chromosomes 4 and 20, likely due to chromosomal inversions. After excluding these putative inversions from the data set, we found weak but non-significant signals of population subdivision. Estimated gene flow revealed reduced rates of migration in areas with extensive habitat fragmentation, while areas with more habitat continuity showed higher rates of genetic connectivity. Our findings provide a benchmark for wildlife managers to compare and scale the genetic diversity and structure of ruffed grouse populations in Pennsylvania and across the eastern USA, as well as reveal unexpected structural variation in the grouse genome that requires further study to understand its possible effects on individual fitness and population distribution.

Organic photovoltaics (OPV) has attracted tremendous attention as a promising alternative to silicon wafer-based technologies for building integration. While significant progress has been achieved on the power conversion efficiency of OPV technologies, their field stability is rarely studied. This work investigates the field performance and reliability of a large-area OPV module designed for BIPV application in the tropical region of Singapore for 4.5 years. The device suffered more than 14% degradation in power at the standard testing conditions from the initial performance, largely due to losses in fill factor (-12% relative). During the monitoring period, it exhibited comparable performance to more conventional silicon PV technologies, with an average specific energy yield of about 4 kWh/kWp/day and an average performance ratio of 0.96. Excellent performance at low light conditions was also observed. However, its field performance was heavily impacted by soiling, which typically led to a 5 to 10% loss in the current output after several months. Further, the device’s outdoor performance also showed a three-stage degradation process, including (1) an initial slow degradation in the first two years (about -1%/year), (2) a stable period with negligible performance loss from year 2 to year 3.5 and (3) a rapid degradation in the last year (about -5%/year).

The fault diagnosis with one single sensor cannot comprehensively use the multi-sensor correlation information of fault signals for underwater thruster entanglement. To solve the real-time fault diagnosis problem that in the case of entanglement, a kind of fault diagnosis method based on the combination of current and rotational speed signal correlation analysis and Fuzzy C-means clustering (FCM) is proposed. Firstly, the collected current and speed signals of underwater thrusters under different states are normalized; Secondly, the correlation of normalized current and speed signals is calculated, and the correlation matrix is formed, and the sliding window of sampling series is adopted to calculate the correlation coefficient change over time between current and speed. Then a kind of Fuzzy C-means clustering method based on improved distance index is used to diagnose fault with correlation matrix elements. To verify the effectiveness of the proposed method, the sampling data in the case of propeller entanglement is used to verify the proposed fault detection method. The results show that the proposed method can fully extract the multi-sensor correlation information of underwater thrusters compared with the fault diagnosis method using only one signal using Support Vector Machine, and the feature extraction is more sufficient, effectively improving the accuracy of underwater thruster fault diagnosis.

Objectives: To explore roles of NKG2D in interactions of CD4 + T cells and dendritic cells (DCs) in juvenile idiopathic arthritis (JIA) patients Methods: Peripheral blood from active JIA patients and healthy controls were used for flow cytometry assessments of NKG2D +CD4 + T cells and expressions of MICA and MICB on DCs. NKG2D genetically modified CD4 + T cells resulted from transfection with lentiviral vectors harboring NKG2D and NKG2D siRNA. ELISA measured supernatant cytokines in co-cultured CD4 + T cells and DCs. CD4 + T cell subgroups, MICA and MICB expression on DCs was determined by flow cytometry and transcription factors by real-time PCR. Results: All JIA patients had significantly higher content of CD4 +NKG2D + T cells compared to healthy controls (P < 0.01). Expression of NKG2D on CD4 + T cells, and MICA and MICB on DCs were significantly greater in articular JIA than systemic JIA (P < 0.05). NKG2D induced IL-12 and suppressed IL-10 and TGF-β from CD4 + T cells, increased IFN-γ + CD4+ T and IL-17 + CD4 + T cells, RORc and T-bet, but reduced CD25 + Foxp3 + CD4 + T cells, IL-4 + CD4 + T cells, Foxp3, and GATA3 in JIA patients (P<0.05). NKG2D increased IL-12 and T-bet by CD4 + T cells in healthy controls (P<0.05). NKG2D decreased IL-10 and increased CD83, MICA, and MICB of DCs via interaction with CD4 + T cells in JIA and control patients (P<0.05). Conclusions: NKG2D regulates differentiation of CD4 + T cells directly and the maturation of DCs indirectly. Targeting NKG2D may be a potential therapy for JIA.

Splenic infarction is a rare and likely underdiagnosed complication of Epstein-Barr virus (EBV) associated infectious mononucleosis (IM). Here we describe an 18-year-old male with persistent severe left-sided abdominal pain found to be EBV positive and have a large splenic infarct, along with a transient decrease in protein C, protein S, and antithrombin III activity levels. He was treated with supportive care, and anticoagulated with heparin and apixaban. We review prior reports and perspectives on underlying pathophysiology, diagnosis, and the management of these cases which likely does not require anticoagulation although may be considered on a per case basis.