Subsequent Phase Research Articles

Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis.

The use of Non-Pharmaceutical Interventions (NPIs), such as lockdowns, social distancing and school closures, against the COVID-19 epidemic is debated, particularly for the possible negative effects on vulnerable populations, including children and adolescents. This study therefore aimed to quantify the impact of NPIs on the trend of pediatric hospitalizations during 2 years of pandemic compared to the previous 3 years, also considering two pandemic phases according to the type of adopted NPIs. This is a multicenter, quasi-experimental before-after study conducted in 12 hospitals of the Emilia-Romagna Region, Northern Italy, with NPI implementation as the intervention event. The 3 years preceding the beginning of NPI implementation (in March 2020) constituted the pre-pandemic phase. The subsequent 2 years were further subdivided into a school closure phase (up to September 2020) and a subsequent mitigation measures phase with less stringent restrictions. School closure was chosen as delimitation as it particularly concerns young people. Interrupted Time Series (ITS) regression analysis was applied to calculate Hospitalization Rate Ratios (HRR) on the diagnostic categories exhibiting the greatest variation. ITS allows the estimation of changes attributable to an intervention, both in terms of immediate (level change) and sustained (slope change) effects, while accounting for pre-intervention secular trends. Overall, in the 60 months of the study there were 84,368 cases. Compared to the pre-pandemic years, statistically significant 35 and 19% decreases in hospitalizations were observed during school closure and in the following mitigation measures phase, respectively. The greatest reduction was recorded for "Respiratory Diseases," whereas the "Mental Disorders" category exhibited a significant increase during mitigation measures. ITS analysis confirms a high reduction of level change during school closure for Respiratory Diseases (HRR 0.19, 95%CI 0.08-0.47) and a similar but smaller significant reduction when mitigation measures were enacted. Level change for Mental Disorders significantly decreased during school closure (HRR 0.50, 95%CI 0.30-0.82) but increased during mitigation measures by 28% (HRR 1.28, 95%CI 0.98-1.69). Our findings provide information on the impact of COVID-19 NPIs which may inform public health policies in future health crises, plan effective control and preventative interventions and target resources where needed.

Validation of Diets with Tomato Pomace in Complete Cycle Breeding of Tenebrio molitor (L.) (Coleoptera: Tenebrionidae).

By-product-based diets have the potential to improve the environmental and economic sustainability of Tenebrio molitor (Linnaeus, 1758) production. However, evaluations of the efficacy of new diets are generally focused on larval performance, while the effect on adults is poorly understood. This aim of this study was to evaluate diets enriched with tomato pomace over a complete breeding cycle. The results showed that when used as an oviposition substrate, all the tested diets, including tomato pomace (T), outperformed the control bran-yeast diet (WY, 95:5 ratio), possibly due to the presence of cholesterol and linoleic acid. The adults fed with the bran-tomato pomace-brewer's spent grain diet (WTB, 50:27:23 ratio), the bran-tomato pomace-yeast diet (WTY, 50:41:9 ratio), and the bran-tomato pomace diet (WT, 50:50 ratio) produced significantly more larvae than those fed with the WY diet. The WTB diet (despite being yeast-free) performed similarly to the WY control diet during the subsequent larval growth phase, making it suitable for the entire production cycle. In conclusion, the results show that tomato pomace can be used a valid by-product in the formulation of efficient diets for the breeding of T. molitor and also provide an alternative to expensive yeast.

Bamboo structures: Innovative methods and applications for structural health monitoring and dissemination

Bamboo is gaining more and more attention in the field of building materials thanks to its unique characteristics, such as resistance, lightness, sustainability, and flexibility. However, the widespread adoption of bamboo as a building material presents some challenges, especially in relation to the durability of the material. In fact, being a natural material, it requires protection from atmospheric agents, such as rain and sun, as well as from attacks by insects and animals. Ensuring the longevity of bamboo involves implementing tailored treatments, precise precautions, and regular structural inspections to detect any potential degradation over time. In this context, the article focused on the monitoring of a structure made of bamboo and on the testing of an innovative system to identify the structural critical issues present, in the context of a FISR project which envisaged a cultural exchange between China and Italy. Starting from the survey of the structure carried out with a drone, a 3D model was then built using a commercial software. For the monitoring phase of the structure, an innovative system was used that exploits the YOLO v5s6 algorithm to identify structural critical issues, supported by a Virtual/Augmented/Mixed reality app developed by the authors. This app is useful not only during the monitoring phase, but also in the subsequent phase of disseminating information relating to the potential of the use of bamboo in the field of structural engineering. The tested and proposed methodologies have proven to be particularly useful and high-performance, especially regarding the monitoring of bamboo structures, in relation to the resolution of problems linked to their possible fragility and limitations in certain application areas. The contribution provided in this paper by Geomatics methodologies is evident to better highlight the potential of this eco-friendly material for its desirable ever-increasing use in the construction sector by becoming aware of its potential.

Heat strain in professional firefighters: physiological responses to a simulated smoke dive in extremely hot environments and the subsequent recovery phase.

Firefighters risk heat strain during occupational tasks when exposed to extremely hot environmental conditions and performing high-intensity work. Relevant training scenarios are therefore essential. This study investigated the effect of a single simulated smoke dive and the following recovery phase on physiological and perceptual responses. Nineteen professional male firefighters (43 ± 8 yr) performed a 2-min stair walk and a15-min simulated smoke dive in a two-floor heat chamber (110°C to 272°C) (HEAT), followed by a 5-min stair walk outside the heat chamber. Heart rate (HR), gastrointestinal temperature (Tgi) and skin temperatures were registered continuously during the test. The Tgi increased significantly from the start (37.5 ± 0.3°C) to the end of HEAT (38.4 ± 0.4°C) and further increased after the heat exposure (39.6 ± 0.5°C). The HR also increased significantly from the start (92 ± 14 bpm) to the end of HEAT (185 ± 13 bpm) and increased after the heat exposure to a maximum of 190 ± 13 bpm. The simulated smoke dive induced high physiological strain on the firefighters, and the increase in Tgi and HR after the hot exposure must be considered during live fire events when repeated smoke dives are required.

Deep learning for enhanced brain Tumor Detection and classification

The purpose of this research is to build an automated, robust, intelligent and hybrid system for the early diagnosis and classifying of brain tumor. To serve this purpose, the authors propose the Auto Contrast Enhancer, Tumor Detector and Classifier to efficiently provide on-demand contrast improvement of poor contrast MRI images for the early diagnosis and classification of brain tumors. The classifier accomplishes its task through a two-phase approach. During the initial phase, ODTWCHE is employed to enhance image contrast, facilitating accurate diagnosis of brain tumors. In the subsequent phase, the classifier leverages the power of deep transfer learning, utilizing the pre-trained Inception V3 model to refine the diagnostic process further. tumor classification. Compared to state-of-the-art models, including AlexNet, VGG-16, DenseNet-201, VGG-19, GoogLeNet, and ResNet-50, the proposed system showcased its outstanding performance by achieving the highest accuracy of 98.89 % on a public dataset that consists of MRI images with varying contrast and brightness levels. The precise detection and classification achieved on this multicolored dataset prove the system's robustness. The authors of the article address the usage of metrics in a variety of contexts, including academia, as well as the possible problems that may result from their improper application. They emphasize how crucial it is to create measurements that align with the system's objectives and to reduce any negative consequences that can skew the data or allow people to manipulate the system's incentives. The authors provide a thorough process for creating metrics that takes into account design considerations, countermeasures for unfavorable effects, and crucial requirements. The paper provides answers for the creation of metrics and gives examples of metrics' failures in many fields. The authors emphasize the significance of understanding how the goal and the data at hand relate to one another, as well as the necessity of compromise and clarity when goals are contradictory or incoherent.A comparative analysis with existing models further confirms that the proposed system consistently outperforms the competition.

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools.

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi.

Early dementia detection with speech analysis and machine learning techniques

This in-depth study journey explores the context of natural language processing and text analysis in dementia detection, revealing their importance in a variety of fields. Beginning with an examination of the widespread and influence of text data. The dataset utilised in this study is from TalkBank's DementiaBank, which is basically a vast database of multimedia interactions built with the goal of examining communication patterns in the context of dementia. The various communication styles dementia patients exhibit when communicating with others are seen from a unique perspective by this specific dataset. Thorough data preprocessing procedures, including cleansing, tokenization, and structuring, are undertaken, with a focus on improving prediction capabilities through the combination of textual and non-textual information in the field of feature engineering. In the subsequent phase, the precision, recall, and F1-score metrics of Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Random Forest, and Artificial Neural Networks (ANN) are assessed. Empirical facts are synthesized using text analysis methods and models to formulate a coherent conclusion. The significance of text data analysis, the revolutionary potential of natural language processing, and the direction for future research are highlighted in this synthesis. Throughout this paper, readers are encouraged to leverage text data to embark on their own adventures in the evolving, data-centric world of dementia detection.

Implementation of a Supervisor-Led Resilience Training Extension in the Royal Military College

Resilience training offered to military personnel often fails to reinforce training practices into routine military activities. We describe the implementation of a supervisor-led after action review designed to provide personnel with frequent opportunities for the supervisor reinforced application of resilience training skills at work. This paper provides a roadmap for engaging supervisors to support this program and details strategies used across its design and their contribution to implementation fidelity. Strategies included iterative stakeholder engagement in co-design and the use of mixed-methods data collection, including field observational ratings, open ended survey questions, and focus group interviews. The design and implementation process are reported in two phases: initial and re-implementation phases. Chi-square analyses and t-tests of initial phase data indicated that the after action review designed to guide the application of resilience training skills was distinct from preexisting after action reviews of tactical skills and drills. Reflexive interpretations of instructor feedback guided refinement to the extension and subsequent re-implementation phase data indicated implementation fidelity. When engaging leaders and resilience non-specialists in activities that support military resilience training, we recommend involving stakeholders in the design and implementation process, responding flexibly to their concerns, and balancing their experienced work change demands with appropriate resources.

Exotic Magnetism in Perovskite KOsO_{3}.

A new perovskite KOsO_{3} has been stabilized under high-pressure and high-temperature conditions. It is cubic at 500K (Pm-3m) and undergoes subsequent phase transitions to tetragonal at 320K (P4/mmm) and rhombohedral (R-3m) at 230K as shown from refining synchrotron x-ray powder diffraction (SXRD) data. The larger orbital overlap integral and the extended wave function of 5d electrons in the perovskite KOsO_{3} allow to explore physics from the regime where Mott and Hund's rule couplings dominate to the state where the multiple interactions are on equal footing. We demonstrate an exotic magnetic ordering phase found by neutron powder diffraction along with physical properties via a suite of measurements including magnetic and transport properties, differential scanning calorimetry, and specific heat, which provide comprehensive information for a system at the crossover from localized to itinerant electronic behavior.

Consistent efficacy outcomes between phase 2 and phase 3 trialsin Crohn's disease or ulcerative colitis in adults: a meta-analysis.

The approval of novel biologic agents and small molecules for the treatment of Crohn's disease (CD) and ulcerative colitis (UC) is dependent on phase 3 randomized controlled trials (RCTs). However, these trials sometimes fail to achieve the expected efficacy outcomes observed in phase 2 trials. We conducted a systematic review of RCTs that evaluated biologic agents and small molecules using paired regimens in both phase 2 and phase 3. We searched Medline, EMBASE, and Cochrane databases up until February 13, 2024. The revised Cochrane tool was utilized to assess the risk of bias. A generalized linear mixed-effects model (GLMM) was employed to estimate the odds ratios (ORs) for efficacy outcomes in phase 2 trials compared to phase 3. We identified a total of 23 trials with 10 paired regimens for CD and 30 trials with 11 paired regimens for UC. The GLMM analysis revealed that phase 2 CD trials had higher outcomes measured by the Crohn's Disease Activity Index (CDAI) by 9-13% without statistical significance: CDAI-150: OR, 1.12 (95% CI 0.83-1.51, p = 0.41); CDAI-100: OR, 1.09 (95% CI 0.88-1.35, p = 0.40); or CDAI-70: OR, 1.13 (95% CI 0.61-2.08, p = 0.66). For UC, two efficacy outcomes were estimated to be equally reported in phase 2/phase 3 pairs: clinical remission: OR, 1.00 (95% CI 0.83-1.20, p = 0.96); endoscopic improvement: OR, 0.98 (95% CI 0.83-1.15, p = 0.79). However, the rate of clinical response was underestimated in phase 2 by 19%: OR, 0.81 (95% CI 0.70-0.95, p = 0.03). The inclusion criterion for the type of Mayo score for UC had a significant interaction with the study phase to influence the difference in clinical response (p = 0.002). Our findings suggest that the main efficacy outcomes for CD and UC remain consistent between phase 2 and phase 3 trials, except for UC response rates. The efficacy data obtained from phase 2 trials can be considered reliable for the design of subsequent phase 3 trials. PROSPERO (CRD42023407947).

Me or we? Action-outcome learning in synchronous joint action

Goal-directed behaviour requires mental representations that encode instrumental relationships between actions and their outcomes. The present study investigated how people acquire representations of joint actions where co-actors perform synchronized action contributions to produce joint outcomes in the environment. Adapting an experimental procedure to assess individual action-outcome learning, we tested whether co-acting individuals link jointly produced action outcomes to individual-level features of their own action contributions or to group-level features of their joint action instead. In a learning phase, pairs of participants produced musical chords by synchronizing individual key press responses. In a subsequent test phase, the previously produced chords were presented as imperative stimuli requiring forced-choice responses by both pair members. Stimulus-response mappings were systematically manipulated to be either compatible or incompatible with the individual and joint action-outcome mappings of the preceding learning phase. Only joint but not individual compatibility was found to modulate participants' performance in the test phase. Yet, opposite to predictions of associative accounts of action-outcome learning, jointly incompatible mappings between learning and test phase resulted in better performance. We discuss a possible explanation of this finding, proposing that pairs' group-level learning experience modulated how participants encoded ambiguous task instructions in the test phase. Our findings inform current debates about mechanistic explanations of action-outcome learning effects and provide novel evidence that joint action is supported by dedicated mental representations encoding own and others' actions on a group level.

Detection of myocardial infarction using Shannon energy envelope, FA-MVEMD and deterministic learning

Myocardial infarction (MI) poses a significant clinical challenge, necessitating expeditious and precise detection to mitigate potentially fatal outcomes. Current MI diagnosis predominantly relies on electrocardiography (ECG); however, it is fraught with limitations, including inter-observer variability and a reliance on expert interpretation. This study introduces an automated MI detection framework that capitalizes on hybrid signal processing methodologies and deterministic learning theory. The initial step involves the extraction of the Shannon energy envelope (SEE) and its derivative from a single-lead ECG. Integration of the SEE into the ECG’s phase portrait provides a means to capture the underlying nonlinear system dynamics. Subsequently, the application of fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) yields discriminative features originating from the most energetically dominant intrinsic mode components (IMFs) within the SEE. Profound dissimilarities are discernible between ECG signals recorded from healthy subjects and those afflicted with MI. In the subsequent phase, deterministic learning theory, implemented through neural networks, is employed to facilitate the classification of ECG signals into two distinct groups. The method’s efficacy is meticulously evaluated using the PTB diagnostic ECG database, resulting in a noteworthy average classification accuracy of 99.21%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} within a tenfold cross-validation framework. In summation, the findings affirm that the proposed features not only complement conventional ECG attributes but also align closely with the underlying dynamics of the ECG system, ultimately fortifying the automatic detection of MI. The imperative requirement for early and accurate MI diagnosis is addressed through our approach, offering a robust and dependable means to fulfill this pivotal clinical need.

Abstract CT057: Phase 1/2 trial of trametinib + ceritinib in patients with unresectable melanoma

Abstract Background: In advanced melanoma, treatment options after progression on immunotherapy are limited, particularly for patients with BRAF-WT tumors. MEK-inhibitors have been explored, with limited clinical efficacy. Our pre-clinical data demonstrated efficacy of the tyrosine kinase inhibitor ceritinib in NRAS-mutant and BRAF-mutant melanoma cells lines. There was synergy observed combining the MEK inhibitor trametinib with ceritinib (compared to trametinib alone) in BRAF-WT melanoma xenografts, with dual suppression of MAPK and mTOR signaling (Verduzco et al, Mol Cancer Ther 2018) Further, in our anti-PD-1 resistant mouse melanoma models, combination of ceritinib and trametinib was highly effective when administered subsequent to anti-PD1. (Phadke et al, Cancer Immunol Res 2021) Methods: We conducted a phase 1/2 trial of ceritinib monotherapy and then combination of trametinib with ceritinib in patients with advanced melanoma after failure of anti-PD-1 (and prior BRAF/MEK inhibitors if eligible.) Primary objective was to identify the maximum tolerated dose (MTD) of this combination, with secondary objectives of tumor response (RECIST 1.1) and progression-free (PFS) and overall survival (OS). In the initial phase 2 portion to explore single-agent activity, patients received oral ceritinib monotherapy at 450 mg daily (QD). In the subsequent phase 1 portion, patients started at dose level (DL)1 of 300 mg PO QD ceritinib + 2mg PO QD trametinib, with a BOIN design to guide dose escalation in cohort sizes of 3. DL 2 was 450 mg + 2 mg QD, while DL -1 was 300 mg + 1.5 mg QD of the combination. Tumor biopsies and blood samples were obtained on day 1 and day 15. Results: We enrolled 28 patients with unresectable/metastatic melanoma. Median age was 66, 10 female, 26 stage IV, 11 NRAS-mutant, 4 BRAF V600-mutant, with median 3 prior lines of prior therapy. There was no objective response observed to ceritinib monotherapy in 11 patients; prolonged stable disease of 6 and 8 months was seen in 2 pts. Median PFS was 2.1 months (95% CI 1.91, NA); median OS was 7.3 months (95% CI 2.4, NA). Two patients had grade 3 AST/ALT elevation; there were no unexpected toxicities. Of the 17 patients on the combination regimen, 12 received DL1. Dose-limiting toxicities of grade 3 rash and grade 2 glaucoma led to de-escalating to DL -1 for other patients. Overall, most frequent grade 3/4 toxicities were grade 3 rash (n=3), grade 3 hypertension (n=2), and grade 3 ALT/AST elevation (n=2). MTD was determined at DL -1 of 1.5 mg trametinib + 300 mg ceritinib QD. Two patients had partial response (12%), both NRAS-mutant; 6 had SD (3 lasting ≥6 months). Median PFS was 3.1 months (95% CI 1.94, 6.18) with median OS of 7.8 months (95% CI 5.1, 13.2). Overall, patients with immediate prior line of anti-PD-1 before trial treatment had a trend toward improved OS (10.2 months vs 5.1 months; p=0.21); no differences were seen based on NRAS or BRAF-status. Conclusions: Ceritinib does not appear to have meaningful single agent activity in treatment-refractory advanced melanoma. Efficacy of the combination with trametinib is modest, and the inability to dose-escalate the combination due to overlapping toxicities such as rash may have contributed. Analyses from baseline and on-treatment tumor biopsies, including immunohistochemistry and single cell RNA seq to explore drug effect on mTOR and MAPK pathways as well as immune infiltration, are ongoing and will be presented. Citation Format: Zeynep Eroglu, Vivien Yin, Joseph Markowitz, Andrew Brohl, Ahmad A. Tarhini, Wenyi Fan, Deanryan De Aquino, Ann Chen, Uwe Rix, Nikhil I. Khushalani, Keiran Smalley. Phase 1/2 trial of trametinib + ceritinib in patients with unresectable melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT057.

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal.

Ultrasonic diagnostics is the earliest way to predict industrial faults. Usually, a contact microphone is employed for detection, but the recording will be contaminated with noise. In this paper, a dataset that contains 10 main faults of pipelines and motors is analyzed from which 30 different features in the time and frequency domains are extracted. Afterward, for dimensionality reduction, principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are performed. In the subsequent phase, recursive feature elimination (RFE) is employed as a strategic method to analyze and select the most relevant features for the classifiers. Next, predictive models consisting of k-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM) are employed. Then, in order to solve the classification problem, a stacking classifier based on a meta-classifier which combines multiple classification models is introduced. Furthermore, the k-fold cross-validation technique is employed to assess the effectiveness of the model in handling new data for the evaluation of experimental results in ultrasonic fault detection. With the proposed method, the accuracy is around 5% higher over five cross folds with the least amount of variation. The timing evaluation of the meta model on the 64 MHz Cortex M4 microcontroller unit (MCU) revealed an execution time of 11 ms, indicating it could be a promising solution for real-time monitoring.

Investigation of thermodynamic state evolution, phase transition and mixing of the hydrocarbon droplet in convective supercritical environments

Liquid alkane droplets transit to supercritical fluids under supercritical conditions. However, the subsequent thermodynamic state evolution, phase transition, and mixing in this case, which directly influences combustion in actual diesel engines, need further investigation. We studied the thermodynamic state evolution, phase transition, and mixing of n-heptane droplets in supercritical laminar nitrogen streams through a set of conservation equations. Non-ideal thermodynamic and transport properties were calculated to incorporate the dramatic variation of physical properties in the supercritical regime. We focus on the effects of Reynolds number, ambient pressure, and droplet size on the thermodynamic state evolution, phase transition and mixing of droplets. Results showed that increasing the Reynolds number can accelerate the evolution of thermodynamic states, which leads to the faster transition of droplets from the liquid phase and supercritical fluids to gas-phase mixtures. With the increased Reynolds number, the phase transition rate is increased and the mixing homogeneity is improved due to the enhanced mass transfer. Caused by the negative correlation between mass transfer and ambient pressure, in higher pressure, droplets after the supercritical transition require more time to transition from supercritical fluids to vapor-phase mixtures. Increasing ambient pressure inhibited the droplet phase transition process and deteriorated the mixing homogeneity, but no significant influence was shown on the droplet dynamic evolution process. Smaller droplets transition faster from liquid phase and supercritical fluids to vapor-phase mixtures. The faster thermodynamic state evolution and faster dynamic evolution process of the smaller droplet leads to its more rapid homogeneous mixing with ambient gasses, but the mode of dynamics is independent of the droplet size.

Investigation of long non-coding RNAs in extracellular vesicles from low-volume blood serum specimens of colorectal cancer patients.

Colorectal cancer (CRC) is the second most prevalent cancer type worldwide, which highlights the urgent need for non-invasive biomarkers for its early detection and improved prognosis. We aimed to investigate the patterns of long non-coding RNAs (lncRNAs) in small extracellular vesicles (sEVs) collected from low-volume blood serum specimens of CRC patients, focusing on their potential as diagnostic biomarkers. Our research comprised two phases: an initial exploratory phase involving RNA sequencing of sEVs from 76 CRC patients and 29 healthy controls, and a subsequent validation phase with a larger cohort of 159 CRC patients and 138 healthy controls. Techniques such as dynamic light scattering, transmission electron microscopy, and Western blotting were utilized for sEV characterization. Optimized protocol for sEV purification, RNA isolation and preamplification was applied to successfully sequence the RNA content of sEVs and validate the results by RT-qPCR. We successfully isolated sEVs from blood serum and prepared sequencing libraries from a low amount of RNA. High-throughput sequencing identified differential levels of 460 transcripts between CRC patients and healthy controls, including mRNAs, lncRNAs, and pseudogenes, with approximately 20% being lncRNAs, highlighting several tumor-specific lncRNAs that have not been associated with CRC development and progression. The validation phase confirmed the upregulation of three lncRNAs (NALT1, AL096828, and LINC01637) in blood serum of CRC patients. This study not only identified lncRNA profiles in a population of sEVs from low-volume blood serum specimens of CRC patients but also highlights the value of innovative techniques in biomolecular research, particularly for the detection and analysis of low-abundance biomolecules in clinical samples. The identification of specific lncRNAs associated with CRC provides a foundation for future research into their functional roles in cancer development and potential clinical applications.

Alginate-Based Emulsions and Hydrogels for Extending the Shelf Life of Banana Fruit.

Edible coatings are used to extend the shelf life of various fruit, including bananas (Musa from the Musaceae family). After harvest, bananas reach the ripening and subsequent senescence phase. During senescence, the quality of the fruit deteriorates as it takes on a brown color and the tissue becomes soft. To extend the shelf life of such a fruit, effective methods to delay ripening are required. In this study, an alginate-based emulsion, i.e., an oil-in-water emulsion of lemongrass essential oil in alginate, was used to combine the mechanical properties of hydrocolloids with the water barrier properties of the oil phase. The emulsion was sprayed onto the whole fruit with an airbrush, and calcium chloride was added to promote gelling of the alginate. Compared to the uncoated fruit, coated bananas remained uniform in appearance (peel color) for longer, showed less weight loss, had a delay in the formation of total soluble solids, and in the consumption of organic acids. The shelf life of the coated fruit was extended by up to 11 days, at least 5 days more than uncoated bananas. Overall, the proposed coating could be suitable for reducing the global amount of food waste.

Pharmacokinetics of desflurane uptake and disposition in piglets.

Many respiratory but few arterial blood pharmacokinetics of desflurane uptake and disposition have been investigated. We explored the pharmacokinetic parameters in piglets by comparing inspiratory, end-tidal, arterial blood, and mixed venous blood concentrations of desflurane. Seven piglets were administered inspiratory 6% desflurane by inhalation over 2h, followed by a 2-h disposition phase. Inspiratory and end-tidal concentrations were detected using an infrared analyzer. Femoral arterial blood and pulmonary artery mixed venous blood were sampled to determine desflurane concentrations by gas chromatography at 1, 3, 5, 10, 20, 30, 40, 50, 60, 80, 100, and 120min during each uptake and disposition phase. Respiratory and hemodynamic parameters were measured simultaneously. Body uptake and disposition rates were calculated by multiplying the difference between the arterial and pulmonary artery blood concentrations by the cardiac output. The rates of desflurane body uptake increased considerably in the initial 5min (79.8ml.min-1) and then declined slowly until 120min (27.0ml.min-1). Similar characteristics of washout were noted during the subsequent disposition phase. Concentration-time curves of end-tidal, arterial, and pulmonary artery blood concentrations fitted well to zero-order input and first-order disposition kinetics. Arterial and pulmonary artery blood concentrations were best fitted using a two-compartment model. After 2h, only 21.9% of the desflurane administered had been eliminated from the body. Under a fixed inspiratory concentration, desflurane body uptake in piglets corresponded to constant zero-order infusion, and the 2-h disposition pattern followed first-order kinetics and best fitted to a two-compartment model.

Time-series reconstruction of the molecular architecture of human centriole assembly

Centriole biogenesis, as in most organelle assemblies, involves the sequential recruitment of sub-structural elements that will support its function. To uncover this process, we correlated the spatial location of 24 centriolar proteins with structural features using expansion microscopy. A time-series reconstruction of protein distributions throughout human procentriole assembly unveiled the molecular architecture of the centriole biogenesis steps. We found that the process initiates with the formation of a naked cartwheel devoid of microtubules. Next, the bloom phase progresses with microtubule blade assembly, concomitantly with radial separation and rapid cartwheel growth. In the subsequent elongation phase, the tubulin backbone grows linearly with the recruitment of the A-C linker, followed by proteins of the inner scaffold (IS). By following six structural modules, we modeled 4D assembly of the human centriole. Collectively, this work provides a framework to investigate the spatial and temporal assembly of large macromolecules.

Synthesis and wear characterization of Al7075/molybdenum disulfide/zirconium diboride hybrid composites

AbstractThe manuscript presents the synthesis and dry sliding wear performance of Al7075 alloy‐based composites. Five compositions namely Al7075 alloy, Al7075+3 vol % molybdenum disulfide, Al7075+3 vol % molybdenum disulfide+3 vol % zirconium diboride, Al7075+3 vol % molybdenum disulfide+6 vol % zirconium diboride and Al7075+3 vol % molybdenum disulfide+9 vol % zirconium diboride were stir cast and characterized for hardness, x‐ray diffraction, microstructure, and tribological properties. In‐situ formed zirconium diboride particles increase the hardness of Al7075 alloy whereas molybdenum disulfide shows the opposite trend due to its self‐lubricating nature. X‐ray diffraction analysis identifies subsequent phase particles in the matrix while microstructural images exhibit the dispersion and morphology of reinforcement particles. The wear tests and friction coefficient were analyzed with variations of sliding velocity, normal load, sliding distance, and compositions. Further, the obtained wear results were also correlated with microstructure and wear surface topography. Al7075+3 vol % molybdenum disulfide composite shows the lowest coefficient of friction because of its self‐lubricating nature. Al7075+3 vol % molybdenum disulfide+9 vol % zirconium diboride hybrid composite exhibits the lowest wear rate at high velocity as well as at high load and can be potentially used in manufacturing and tribological applications. Moreover, the use of this material may lead to the minimum requirement of external toxic lubricants that make it cost‐effective and environmentally sustainable.