Validity Of Strategy Research Articles

Flow cytometry assay modifications: Recommendations for method validation based on CLSI H62 guidelines.

The Clinical and Laboratory Standards Institute (CLSI) H62-Validation of Assays Performed by Flow Cytometry guideline, released in 2021, provides recommendations for platform workflow and quality system essentials, instrument setup and standardization, assay development and optimization and fit-for-purpose analytical method validation. In addition, CLSI H62 includes some recommendations for the validation strategies after a validated flow cytometric method has been modified. This manuscript builds on those recommendations and discusses the impact of different types of assay modifications on assay performance. Recommendations regarding which validation parameters to evaluate depending on the type of modification are provided. The impact of assay modification on the assay's intended use is discussed. When recommending minor deviations from the CLSI H62 process for a laboratory-initiated assay revision (e.g., specimen numbers for sensitivity, specificity, or precision studies), a rationale based on expert opinion is provided with the understanding that not every laboratory, assay type, and circumstance can be comprehensively addressed in this paper. These recommendations are meant as a practical recommendation and are not intended to be restrictive, prescriptive, or understood as necessarily sufficient to meet every specific requirement from regulatory bodies (e.g., FDA or New York State Department of Health).

Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning

The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address these issues, this paper presents a physics-constrained gap-filling method, PhyFill for short. The PhyFill method employs a partial convolutional neural network technique to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous spatially daily soil moisture data on a global scale. Three validation strategies are employed: visual inspection through global pattern, simulated missing-region validation, and soil moisture validation with in situ measurements. The results indicated that the reconstructed soil moisture achieved a higher spatial coverage with satisfactory spatial continuity with neighbouring pixels. The simulated validation of the missing regions revealed that the averaged unbiased root mean square difference (ubRMSD) and correlation coefficient (R) were 0.01 m3/m3 and 0.99, respectively versus the gap filled SMAP product. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSD compared with the original SMAP soil moisture data (0.04 m3/m3vs. 0.04 m3/m3). The PhyFill method can generate globally continuous, high accurate soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.

Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods

A systematic review on machine-learning strategies for improving generalization in electroencephalography-based emotion classification was realized. In particular, cross-subject and cross-session generalization was focused. In this context, the non-stationarity of electroencephalographic (EEG) signals is a critical issue and can lead to the Dataset Shift problem. Several architectures and methods have been proposed to address this issue, mainly based on transfer learning methods. In this review, 449 papers were retrieved from the Scopus, IEEE Xplore and PubMed databases through a search query focusing on modern machine learning techniques for generalization in EEG-based emotion assessment. Among these papers, 79 were found eligible based on their relevance to the problem. Studies lacking a specific cross-subject or cross-session validation strategy, or making use of other biosignals as support were excluded. On the basis of the selected papers’ analysis, a taxonomy of the studies employing Machine Learning (ML) methods was proposed, together with a brief discussion of the different ML approaches involved. The studies reporting the best results in terms of average classification accuracy were identified, supporting that transfer learning methods seem to perform better than other approaches. A discussion is proposed on the impact of (i) the emotion theoretical models and (ii) psychological screening of the experimental sample on the classifier performances.

Identification of an optimal ground-based validation site for FLEX and quantification of uncertainties using airborne HyPlant data - A case study in Italy

The Fluorescence Explorer (FLEX) satellite will carry the high-resolution Fluorescence Imaging Spectrometer (FLORIS) that measures the complete fluorescence spectrum emitted by chlorophyll of terrestrial vegetation. This small signal must be validated. One validation approach is comparing the fluorescence signal retrieved from satellite-based measurements with ground based measurements. However, the difference in spatial resolution of the satellite and ground-based instruments and a geolocation mismatch will result in differences in the detected signal and thus, in uncertainties of the validation strategy. In a case study, we identify a representative ground site for validating the fluorescence signal by analyzing surface reflectance measurements from an aeroplane.We define requirements of representativeness for a validation ground site in vegetated areas. Based on those requirements, we identify a suitable position within a case study in central Italy using surface reflectance data from the airborne High-Performance Airborne Imaging Spectrometer (HyPlant) measured in summer 2018. The representativeness is quantified by the relative difference between the single HyPlant pixel representing a ground-based measurement and the averaged signal of several HyPlant pixels that mimics a FLORIS pixel. With this measure, we quantify the validation uncertainty due to spatial resolution and geolocation mismatch. The effect of the temporal evolution of the surface properties on the validation uncertainty due to spatial resolution is investigated.We select the ground site position by minimizing the validation uncertainty due to spatial resolution. Especially for wavelengths larger than 700 nm, this uncertainty is smaller than 2 % for all different reference areas. The largest differences between ground-based like measurement and satellite-like measurement of the surface reflectance is due to geolocation mismatch. The uncertainty due the geolocation mismatch is very large for wavelengths smaller than 720 nm and moderate for wavelengths larger than 720 nm. Thus, the surface reflectance at the chosen position for the validation site is not homogeneous enough for validation purpose. Considering a reference area of 13.5 × 13.5 m2, we quantify temporal stable and small uncertainties for the spectral range between 720 and 800 nm. For an all-embracing validation of the surface reflectance of vegetated areas, the chosen site is not appropriate.

Evaluation of deep learning for predicting rice traits using structural and single-nucleotide genomic variants

BackgroundStructural genomic variants (SVs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider only Single Nucleotide Polymorphisms (SNPs). Deep Learning (DL) is a promising strategy for genomic prediction, but its performance using SVs and SNPs as genetic markers remains unknown.ResultsWe used rice to investigate whether combining SVs and SNPs can result in better trait prediction over SNPs alone and examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models. Specifically, the performances of BayesC (considering additive effects) and a Bayesian Reproducible Kernel Hilbert space (RKHS) regression (considering both additive and non-additive effects) were compared to those of two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network, to explore their prediction ability by using various marker input strategies. We found that exploiting structural and nucleotide variation slightly improved prediction ability on complex traits in 87% of the cases. DL models outperformed Bayesian models in 75% of the studied cases, considering the four traits and the two validation strategies used. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models.ConclusionsOur study reveals that the use of structural genomic variants can improve trait prediction in rice, independently of the methodology used. Also, our results suggest that Deep Learning (DL) networks can perform better than Bayesian models in the prediction of binary traits, and in quantitative traits when the training and target sets are not closely related. This highlights the potential of DL to enhance crop improvement in specific scenarios and the importance to consider SVs in addition to SNPs in genomic selection.

Ultrafast carrier dynamics triggered by Ga-S bond in GaZnON/CdS heterojunction for efficient photocatalytic hydrogen evolution

Constructing heterojunction has been regarded as a valid strategy of photocatalysts to promote the catalytic process. However, beyond the possible synergistic advantage from the component catalysts, the understanding of the interface effect on the photocatalytic process within heterojunction remains challenging. Herein, we prepare a novel GaZnON/CdS heterojunction photocatalyst via a robust electrostatic assembly method. The sulfur atoms in CdS are found to occupy the nitrogen vacancy over the GaZnON surface to form interfacial Ga-S bonds, which is proved as electron-transfer channel between CdS and GaZnON to realize efficient photo-induced carrier separation. The dynamic study quantificationally reveals interfacial electron transfer with a fast transfer rate of 143.8 ns−1. Thereby, the optimal composite photocatalyst reached an impressive hydrogen evolution rate of 14.76 mmol g-1h−1, as well as an impressive AQY of 36.1 % (450 nm). This work expounds the ultrafast carrier dynamics underlying interface bonds within heterojunction for weakened photo-induced carrier recombination.

Peer mentoring experience related to information and communication technologies. A qualitative study

IntroductionQuality and inclusive education must include Information and Communication Technologies (ICT), which is a growing trend in educational environments since the COVID-19 pandemic. These technologies have transformed learning towards more collaborative and interactive models, but they also pose a barrier due to the lack of skills or knowledge about their use. This circumstance has been identified among some first-year nursing students. ObjectiveThe present study focuses on exploring the perception of students who participated in the experience of peer mentoring to improve adaptation to the use of Information and Communication Technologies. Design and methodsThe study employed a descriptive qualitative approach, relying on in-depth interviews and a focus group conducted with 29 students who participated in the project between 2020 and 2023. Additionally, sociodemographic data and information about Information and Communication Technologies were collected through a self-administered questionnaire. ResultsThe findings indicate that mentors exhibited higher competencies than those being mentored, yet the experience was positive and satisfying for both, emerging as an effective strategy to facilitate learning. Another noteworthy result is the performance of younger students in the role of mentors, a fact that contradicts similar experiences. ConclusionIn conclusion, peer mentoring emerges as a valid strategy for acquiring digital competencies and Information and Communication Technologies among first-year nursing students, fostering more inclusive learning environments.

Raman spectroscopy-based authentication of powder goat milk adulteration with cow milk

This study introduces, for the first time, the utilization of a handheld Raman spectrometer in conjunction with Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) for the authentication of powdered goat milk (target class) against adulteration with powdered cow milk. Since the representativeness of the target class is the first critical step to construct a model without overfitting or underfitting, especially when dealing with a few numbers of target samples, three distinct validation strategies (approaches 1, 2, and 3, respectively), incorporating no partitioning of the target samples, Procrustes cross-validation, and Kennard-Stone-based partitioning, were assessed. Moreover, the spectral data underwent preprocessing techniques including offset correction (OFF), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), and Savitzky-Golay derivative (SGD). Achieving a correct classification rate of 100% for all studied samples using MSC for approaches 1 and 2, and SGD for approach 3, underscores the robustness of Raman spectroscopy in furnishing pertinent analytical insights for the authentication of pure powdered goat milk, with a significance level of 0.01. This methodology furnishes distinct spectral signatures associated with the macromolecular composition, particularly fats, proteins, carbohydrates, and carotenoids, facilitating the detection of adulterations as low as 1% w/w of cow milk added to powdered goat milk. Thus, the proposed method offers a rapid and reliable on-site screening tool, consonant with the principles of green food analysis, especially advantageous in situations where identifying the type and/or nature of the adulterants is not feasible.

Age matters: The effect of regret and loss aversion on cultured meat acceptance

Cultured meat shows great promise as a more sustainable alternative to conventional meat consumption. However, consumer acceptance of cultured meat remains a great challenge as studies indicate a general reluctance to adopt this product. Notably, while existing literature has provided various factors influencing consumer acceptance of cultured meat, there is a limited focus on the use of affective cues. The present research examines the impact of regret appeal on consumers' willingness to try cultured meat. In two experimental studies, the authors investigate (1) the interactive effect between regret and age on willingness to try cultured meat, and (2) the role of loss aversion as a mediating factor between regret and willingness to try cultured meat. The results demonstrate the effectiveness of regret appeal in increasing consumers’ willingness to try cultured meat, particularly among older populations. This is because older populations exhibit higher levels of loss aversion. The present study is the first to shed light on the interactive effect of regret and age in influencing sustainable product acceptance. Furthermore, the study establishes the first empirical evidence to demonstrate that loss aversion is a valid self-regulating strategy adopted to cope with the feeling of regret in a consumption context.

Non-isothermal crystallization behavior of Cr50Fe6Co7Mo14C15B6Y2 metallic glass with super high content of Cr and integrated merits

In present work, non-isothermal crystallization behavior for novel Cr50Fe6Co7Mo14C15B6Y2 metallic glass (MG) with super high content of Cr and excellent merits was investigated by differential scanning calorimeter and X-ray diffraction. Both of the Kissinger and Ozawa methods show that the present glass transition is much harder than other transitions for a much larger apparent activation energy (E). While, the growth process of present second crystallization event is the easiest process, since it possesses the smallest E. Meanwhile, the local activation energy established by Kissinger-Akahira-Sunose and Ozawa-Flynn-Wall methods both exhibit a trend of increasing to the maximum value at first and then gradual decrease until the finish of the crystallization, indicating that the difficulty increases until to the maximum at first and then gradually decreases during crystallization. Moreover, all the local Avrami exponent n(x) possesses a value of about 0 <n(x) < 1.5, implying a crystallization with pre-existing nuclei for present non-isothermal crystallization. Furthermore, the glass formation melt of present MG is considered as an intermediate one with moderate thermal stability. Additionally, the alloy firstly changes from monolithic MG to a composite of amorphous matrix superimposed with the main crystalline phases ((Fe,Cr)23(C,B)6, (Cr2.5Fe4.3Mo0.1)C3, Fe3Mo3C, FeC and some unknown phases), and then the remained amorphous matrix thoroughly changes to these crystalline phases with enriched crystal planes and fraction during crystallization. The results can supply basic data for designing valid crystallization strategy to tune performances of metallic glasses with high content of Cr.

The Scavenging Activity of Coenzyme Q10 Plus a Nutritional Complex on Human Retinal Pigment Epithelial Cells.

Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are common retinal diseases responsible for most blindness in working-age and elderly populations. Oxidative stress and mitochondrial dysfunction play roles in these pathogenesis, and new therapies counteracting these contributors could be of great interest. Some molecules, like coenzyme Q10 (CoQ10), are considered beneficial to maintain mitochondrial homeostasis and contribute to the prevention of cellular apoptosis. We investigated the impact of adding CoQ10 (Q) to a nutritional antioxidant complex (Nutrof Total®; N) on the mitochondrial status and apoptosis in an in vitro hydrogen peroxide (H2O2)-induced oxidative stress model in human retinal pigment epithelium (RPE) cells. H2O2 significantly increased 8-OHdG levels (p < 0.05), caspase-3 (p < 0.0001) and TUNEL intensity (p < 0.01), and RANTES (p < 0.05), caspase-1 (p < 0.05), superoxide (p < 0.05), and DRP-1 (p < 0.05) levels, and also decreased IL1β, SOD2, and CAT gene expression (p < 0.05) vs. control. Remarkably, Q showed a significant recovery in IL1β gene expression, TUNEL, TNFα, caspase-1, and JC-1 (p < 0.05) vs. H2O2, and NQ showed a synergist effect in caspase-3 (p < 0.01), TUNEL (p < 0.0001), mtDNA, and DRP-1 (p < 0.05). Our results showed that CoQ10 supplementation is effective in restoring/preventing apoptosis and mitochondrial stress-related damage, suggesting that it could be a valid strategy in degenerative processes such as AMD or DR.

PDE4D: A Multipurpose Pharmacological Target.

Phosphodiesterase 4 (PDE4) enzymes catalyze cyclic adenosine monophosphate (cAMP) hydrolysis and are involved in a variety of physiological processes, including brain function, monocyte and macrophage activation, and neutrophil infiltration. Among different PDE4 isoforms, Phosphodiesterases 4D (PDE4Ds) play a fundamental role in cognitive, learning and memory consolidation processes and cancer development. Selective PDE4D inhibitors (PDE4Dis) could represent an innovative and valid therapeutic strategy for the treatment of various neurodegenerative diseases, such as Alzheimer's, Parkinson's, Huntington's, and Lou Gehrig's diseases, but also for stroke, traumatic brain and spinal cord injury, mild cognitive impairment, and all demyelinating diseases such as multiple sclerosis. In addition, small molecules able to block PDE4D isoforms have been recently studied for the treatment of specific cancer types, particularly hepatocellular carcinoma and breast cancer. This review overviews the PDE4DIsso far identified and provides useful information, from a medicinal chemistry point of view, for the development of a novel series of compounds with improved pharmacological properties.

Non-invasive prehabilitation to foster widespread fMRI cortical reorganization before brain tumor surgery: lessons from a case series.

The objective of this prospective, single-centre case series was to investigate feasibility, clinical outcomes, and neural correlates of non-invasive Neuromodulation-Induced Cortical Prehabilitation (NICP) before brain tumor surgery. Previous studies have shown that gross total resection is paramount to increase life expectancy but is counterbalanced by the need of preserving critical functional areas. NICP aims at expanding functional margins for extensive tumor resection without functional sequelae. Invasive NICP (intracranial neuromodulation) was effective but characterized by elevated costs and high rate of adverse events. Non-invasive NICP (transcranial neuromodulation) may represent a more feasible alternative. Nonetheless, up to this point, non-invasive NICP has been examined in only two case reports, yielding inconclusive findings. Treatment sessions consisted of non-invasive neuromodulation, to transiently deactivate critical areas adjacent to the lesion, coupled with intensive functional training, to activate alternative nodes within the same functional network. Patients were evaluated pre-NICP, post-NICP, and at follow-up post-surgery. Ten patients performed the intervention. Feasibility criteria were met (retention, adherence, safety, and patient's satisfaction). Clinical outcomes showed overall stability and improvements in motor and executive function from pre- to post-NICP, and at follow-up. Relevant plasticity changes (increase in the distance between tumor and critical area) were observed when the neuromodulation target was guided by functional neuroimaging data. This is the first case series demonstrating feasibility of non-invasive NICP. Neural correlates indicate that neuroimaging-guided target selection may represent a valid strategy to leverage neuroplastic changes before neurosurgery. Further investigations are needed to confirm such preliminary findings.

Porous Photocrosslinkable Hydrogel Functionalized with USC Derived Small Extracellular Vesicles for Corpus Spongiosum Repair.

Reconstruction of a full-thickness spongy urethra is difficult because a corpus spongiosum (CS) defect cannot be repaired using self-healing or substitution urethroplasty. Small extracellular vesicles (sEVs) secreted by urine-derived stem cells (USC-sEVs) strongly promote vascular regeneration. In this study, it is aimed to explore whether USC-sEVs promote the repair of CS defects. To prolong the in vivo effects of USC-sEVs, a void-forming photoinduced imine crosslinking hydrogel (vHG) is prepared and mixed with the USC-sEV suspension. vHG encapsulated with USC-sEVs (vHG-sEVs) is used to repair a CS defect with length of 1.5cm and width of 0.8cm. The results show that vHG-sEVs promote the regeneration and repair of CS defects. Histological analysis reveals abundant sinusoid-like vascular structures in the vHG-sEV group. Photoacoustic microscopy indicates that blood flow and microvascular structure of the defect area in the vHG-sEV group are similar to those in the normal CS group. This study confirms that the in situ-formed vHG-sEV patch appears to be a valid and promising strategy for repairing CS defects.

Intersite communication in dimeric enzymes highlighted by structural and thermodynamic analysis of didansyltyrosine binding to thymidylate synthases

Intersite communication in dimeric enzymes, triggered by ligand binding, represents both a challenge and an opportunity in enzyme inhibition strategy. Though often understestimated, it can impact on the in vivo biological mechansim of an inhibitor and on its pharmacokinetics. Thymidylate synthase (TS) is a homodimeric enzyme present in almost all living organisms that plays a crucial role in DNA synthesis and cell replication. While its inhibition is a valid strategy in the therapy of several human cancers, designing specific inhibitors of bacterial TSs poses a challenge to the development of new anti-infective agents. N,O-didansyl-l-tyrosine (DDT) inhibits both Escherichia coli TS (EcTS) and Lactobacillus casei TS (LcTS). The available X-ray structure of the DDT:dUMP:EcTS ternary complex indicated an unexpected binding mode for DDT to EcTS, involving a rearrangement of the protein and addressing the matter of communication between the two active sites of an enzyme dimer. Combining molecular-level information on DDT binding to EcTS and LcTS extracted from structural and FRET-based fluorometric evidence with a thermodynamic characterization of these events obtained by fluorometric and calorimetric titrations, this study unveiled a negative cooperativity between the DDT bindings to the two monomers of each enzyme dimer. This result, complemented by the species-specific thermodynamic signatures of the binding events, implied that communication across the protein dimer was triggered by the first DDT binding. These findings could challenge the conventional understanding of TS inhibition and open the way for the development of novel TS inhibitors with a different mechanism of action and enhanced efficacy and specificity.

Porphyromonas gingivalis virulence factors induce toxic effects in SH-SY5Y neuroblastoma cells: GRK5 modulation as a protective strategy

Periodontitis (PDS) is a chronic inflammatory disease initiated by a dysbiosis of oral pathogenic bacterial species, such as Porphyromonas gingivalis (Pg). These bacteria can penetrate the bloodstream, releasing various endo and exotoxins that fuel the infection, and stimulate toxic inflammation in different compartments, including the brain. However, the specific mechanisms by which PDS/Pg contribute to brain disorders, such as Alzheimer’s disease (AD), remain unclear. This study assessed the effects of Pg’s virulence factors - lipopolysaccharide (LPS-Pg) and gingipains (gps) K (Kgp) and Rgp - on SH-SY5Y cells. Our results demonstrated that LPS-Pg activated signaling through the Toll-like receptor (TLR)-2/4 induced a significant downregulation of G protein-coupled receptor kinase 5 (GRK5). Additionally, LPS-Pg stimulation resulted in a robust increase in Tau phosphorylation (pTau) and p53 levels, while causing a marked reduction in Bcl2 and increased cell death compared to unstimulated cells (Ns). LPS-Pg also elevated inducible nitric oxide synthase (iNOS) expression, leading to oxidative damage. In cells overexpressing GRK5 via Adenovirus, LPS-Pg failed to increase iNOS and pTau levels compared to GFP control cells. High GRK5 levels also prevented the nuclear accumulation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB). Furthermore, the overexpression of a GRK5 mutant form lacking the nuclear localization signal (ΔNLS) nearly abolished LPS-Pg induced p53 and iNOS upregulation. Finally, we tested whether Kgp and Rgp mediated similar effects and our data showed that both gps caused a marked downregulation of GRK5 leading to increased p53 and pTau levels.In conclusion, this study provides further insight into the toxic effects elicited by Pg in cells and suggests that preventing GRK5 deficiency may be a valid strategy to mitigate Pg-induced toxic effects (i.e. cell death, oxidative damage, and Tau hyperphosphorylation) in SH-SY5Y cells, which are typical molecular hallmarks of neurodegenerative disorders.

Soil fertility and plant nutrition in an organic olive orchard after 5 years of amendment with compost, biochar or their blend

The agronomic use of compost and biochar as soil amendments may exhibit contrasting results in terms of soil fertility and plant nutrition. The effects of the biennial application of biochar, compost and a blend of compost:biochar (90:10; % dw:dw) on the agronomical performance of an organically managed and well established 25-year-old olive orchard was assessed 5 years after the initial application. The agronomical evaluation was based on the assessment of the soil physical, chemical, and biological characteristics, and the assessment of the soil fertility by both crop production and nutritional status of the orchard, and the bioassay with olive plantlets. Biochar mainly benefited the physical properties (bulk density, total porosity, aeration, water retention capacity) of soil, especially in the top 0–5 cm. Compost and its blend with biochar improved microbial activity, soil nutritional status (increasing the content of soluble organic C, N, and P) and favoured the formation of aggregates in soil. The bioassay conducted with young plantlets confirmed the enhanced soil fertility status in the three amended treatments, particularly in the case of biochar and its blend with compost. However, this effect was not significantly observed in the adult plants after 5 years of application, reflecting the slow response of adult olive trees to changes in fertilization. Based on these results, alongside the desirable long-residence time of biochar in soil and the ready availability of compost, the blend of biochar with compost assayed in this study is defined as a valid strategy for preparing high quality soil organic amendments.

In Vivo Chemical Screening in Zebrafish Embryos Identified FDA-Approved Drugs That Induce Differentiation of Acute Myeloid Leukemia Cells.

Acute myeloid leukemia (AML) is characterized by the abnormal proliferation and differentiation arrest of myeloid progenitor cells. The clinical treatment of AML remains challenging. Promoting AML cell differentiation is a valid strategy, but effective differentiation drugs are lacking for most types of AML. In this study, we generated Tg(drl:hoxa9) zebrafish, in which hoxa9 overexpression was driven in hematopoietic cells and myeloid differentiation arrest was exhibited. Using Tg(drl:hoxa9) embryos, we performed chemical screening and identified four FDA-approved drugs, ethacrynic acid, khellin, oxcarbazepine, and alendronate, that efficiently restored myeloid differentiation. The four drugs also induced AML cell differentiation, with ethacrynic acid being the most effective. By an RNA-seq analysis, we found that during differentiation, ethacrynic acid activated the IL-17 and MAPK signaling pathways, which are known to promote granulopoiesis. Furthermore, we found that ethacrynic acid enhanced all-trans retinoic acid (ATRA)-induced differentiation, and both types of signaling converged on the IL-17/MAPK pathways. Inhibiting the IL-17/MAPK pathways impaired ethacrynic acid and ATRA-induced differentiation. In addition, we showed that ethacrynic acid is less toxic to embryogenesis and less disruptive to normal hematopoiesis than ATRA. Thus, the combination of ethacrynic acid and ATRA may have broader clinical applications. In conclusion, through zebrafish-aided screening, our study identified four drugs that can be repurposed to induce AML differentiation, thus providing new agents for AML therapy.

Exploring the Usefulness of a Multi-Sensory Environment on Sensory Behaviors in Children with Autism Spectrum Disorder.

Background: Autism spectrum disorder (ASD) is a complex neurological development with social and communication deficits and sensory abnormalities. Sensory problems have a significant impact on daily life. Multisensory environments (MSEs), such as Snoezelen® rooms, offer controlled sensory stimulation. This study aims to evaluate the effect of MSE intervention with self-controlled sensory interactions on adaptive developmental skills and sensory responses in preschool ASD children. Methods: This pilot study was single-blind, randomized, controlled, and adhered to the CONSORT guidelines. Twenty participants were recruited and randomized into two groups: the control group (CG) underwent treatment as usual (TAU) with individual rehabilitation sessions of psychomotor therapy. The experimental group (EG) underwent TAU integrated with the use of an MSE. Developmental abilities and severity levels were assessed, pre-post, with the Psychoeducational Profile, Third Edition (PEP-3) and the Second Edition Childhood Autism Rating Scale (CARS-2). Results: A significant difference in taste, smell, and tactile behaviors according to the CARS-2, as well as in gross motor skills according to the PEP-3, was observed in the EG. Conclusions: This pilot study suggests that MSE-integrated intervention may be a valid strategy to improve self-management of the sensory profile in autistic individuals. Further studies are needed to better identify the intervention methodology and effectiveness.

The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature

PurposeTo systematically review the use of artificial intelligence (AI) in musculoskeletal (MSK) ultrasound (US) with an emphasis on AI algorithm categories and validation strategies.Material and MethodsAn electronic literature search was conducted for articles published up to January 2024. Inclusion criteria were the use of AI in MSK US, involvement of humans, English language, and ethics committee approval.ResultsOut of 269 identified papers, 16 studies published between 2020 and 2023 were included. The research was aimed at predicting diagnosis and/or segmentation in a total of 11 (69%) out of 16 studies. A total of 11 (69%) studies used deep learning (DL)-based algorithms, three (19%) studies employed conventional machine learning (ML)-based algorithms, and two (12%) studies employed both conventional ML- and DL-based algorithms. Six (38%) studies used cross-validation techniques with K-fold cross-validation being the most frequently employed (n = 4, 25%). Clinical validation with separate internal test datasets was reported in nine (56%) papers. No external clinical validation was reported.ConclusionAI is a topic of increasing interest in MSK US research. In future studies, attention should be paid to the use of validation strategies, particularly regarding independent clinical validation performed on external datasets.