Search

Introduction: Chimeric Antigen Receptor T-cell therapy (CAR-T) represents the most recent immunotherapy’s innovation to cure some refractory and/or relapsing haematological tumours. However, because of the life-threatening toxicities it might cause such as Cytokine Release Syndrome and Immune Cell Associated Neurotoxicity Syndrome, patients are closely monitored by nurses for the early identification of toxicities during the post-infusion phase of CAR-T cell therapy. Exploring the nurses’ experience with respect to any difficulties related to the monitoring is important since these issues can be perceived by patients and affect the nurse-patient’s caring relationship, considered as a shared lived experience between the patient and the nurse. Aim: This study aims to investigate haematology nurses’ lived experience with monitoring CAR-T’s induced toxicities. Materials and methods: A qualitative study following Cohen's phenomenological methodology will be conducted through semi-structured interviews in a sample of Italian nurses working in haematology units, who have had previous experience in the management of patients undergoing CAR-T therapy for at least two months and who have performed the monitoring for the same months of experience; the interviews will be audio-recorded and then transcribed verbatim. Two researchers will carry out the manual analysis and interpretation of the collected data independently, identifying themes and sub-themes. Conclusion: To explore the nurses’ experiences in this field could facilitate the identification of the educational needs, at individual and group level. Despite it is important to consider contextual variables, the findings of this study could contribute to develop evidence supporting advanced and specialized nursing care in the haematological setting.

Abstract Intestinal parasitic infections (IPIs) can lead to significant morbidity and mortality in cancer patients. While they are unlikely to cause severe disease and are self-limiting in healthy individuals, cancer patients are especially susceptible to opportunistic parasitic infections. The gut microbiota plays a crucial role in various aspects of health, including immune regulation and metabolic processes. Parasites occupy the same environment as bacteria in the gut. Recent research suggests intestinal parasites can disrupt the normal balance of the gut microbiota. However, there is limited understanding of this co-infection dynamic among cancer patients in Malaysia. A study was conducted to determine the prevalence and relationship between intestinal parasites and gut microbiota composition in cancer patients. Stool samples from 134 cancer patients undergoing active treatment or newly diagnosed were collected and examined for the presence of intestinal parasites and gut microbiota composition. The study also involved 17 healthy individuals for comparison and control. Sequencing with 16S RNA at the V3–V4 region was used to determine the gut microbial composition between infected and non-infected cancer patients and healthy control subjects. The overall prevalence of IPIs among cancer patients was found to be 32.8%. Microsporidia spp. Accounted for the highest percentage at 20.1%, followed by Entamoeba spp. (3.7%), Cryptosporidium spp. (3.0%), Cyclospora spp. (2.2%), and Ascaris lumbricoides (0.8%). None of the health control subjects tested positive for intestinal parasites. The sequencing data analysis revealed that the gut microbiota diversity and composition were significantly different in cancer patients than in healthy controls (p

Abstract Background Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease’s spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model—but with improved accuracy—for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector. Methods The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3–C5) Res-block bodies of the backbone architecture’s C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed. Results The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23 MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone. Conclusions The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model’s performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision. Graphical Abstract

Background Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable, and standardised analysis, leading to enhanced understanding, diagnosis, and control of bacterial-related phenomena. Methods Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies. Results The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively. Conclusions This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models.

The gold standard for diagnosing malaria remains microscopic examination; however, its application is frequently impeded by the lack of a standardized framework that guarantees uniformity and quality, particularly in scenarios with limited resources and high volume. This study suggests a novel and highly effective automated diagnostic approach that employs deep-learning object detectors to improve the accuracy and efficiency of malaria-infected cell detection and Plasmodium species classification to overcome these challenges. Plasmodium parasites were detected within thin blood stain images using the YOLOv4 and YOLOv5 models, which were optimized for this purpose. YOLOv5 obtains a slightly higher accuracy on the source dataset (mAP@ $0.5=96$ %) than YOLOv4 (mAP@ $0.5=89$ %), but YOLOv4 exhibits superior robustness and generalization across diverse datasets, as demonstrated by its performance on an independent validation set (mAP@ $0.5=90$ %). This robustness emphasizes the dependability of YOLOv4 for deployment in a variety of clinical settings. Furthermore, an automated process was implemented to produce bound single-cell images from YOLOv4’s localization outputs, thereby eradicating the necessity for conventional and time-consuming segmentation methods. The DenseNet-121 model, which was optimized for species identification, obtained an impressive overall accuracy of 95.5% in the subsequent classification stage, indicating excellent generalization across all malaria species. Accurate classification of Plasmodium species on microscopically thin blood films is essential for guiding appropriate therapy and preventing unnecessary anti-malarial treatments, which can lead to adverse effects and contribute to drug resistance. This research contributes to the field of automated malaria diagnosis by offering a comprehensive framework that substantially improves clinical decision-making, particularly in resource-limited environments.

Receptors for activated C kinases (RACKs) have been shown to coordinate PKC-mediated hypertrophic signalling in mice. However, little information is available on its participation in embryonic gene expression. This study investigated the involvement of RACK1 in the expression of embryonic genes in a zebrafish (ZF) ex vivo heart culture model by using phenylephrine (PE) or a growth factors cocktail (GFs) as a prohypertrophic/regeneration stimulus. Blebbistatin (BL) inhibition has also been studied for its ability to block the signal transduction actions of some PEs. qRT‒PCR and immunoblot analyses confirmed the upregulation of RACK1 in the PE- and GFs-treated groups. BL administration counteracted PE-induced hypertrophy and downregulated RACK1 expression. Immunohistochemical analyses of the heart revealed the colocalization of RACK1 and embryonic genes, namely, Gata4, Wt1, and Nfat2, under stimulation, whereas these genes were expressed at lower levels in the BL treatment group. Culturing ZF heart cells activated via GFs treatment increased the expression of RACK1. The overexpression of RACK1 induced by the transfection of recombinant RACK1 cDNA in ZF heart cells increased the expression of embryonic genes, especially after one week of GFs treatment. In summary, these results support the involvement of RACK1 in the induction of embryonic genes during cardiac hypertrophy/GFs stimulation in a fish heart model, which can be used as an alternative study model for mammals., (© 2024. The Author(s).)

Understanding the sub-band gap luminescence in Ruddlesden-Popper 2D metal halide hybrid perovskites (2D HaPs) is essential for efficient charge injection and collection in optoelectronic devices. Still, its origins are still under debate with respect to the role of self-trapped excitons or radiative recombination via defect states. In this study, we characterized charge separation, recombination, and transport in single crystals, exfoliated layers, and polycrystalline thin films of butylammonium lead iodide (BA 2 PbI 4 ), one of the most prominent 2D HaPs. We combined complementary defect- and exciton-sensitive methods such as photoluminescence (PL) spectroscopy, modulated and time-resolved surface photovoltage (SPV) spectroscopy, constant final state photoelectron yield spectroscopy (CFSYS), and constant light-induced magneto transport (CLIMAT), to demonstrate striking differences between charge separation induced by dissociation of excitons and by excitation of mobile charge carriers from defect states. Our results suggest that the broad sub-band gap emission in BA 2 PbI 4 and other 2D HaPs is caused by radiative recombination via defect states (shallow as well as midgap states) rather than self-trapped excitons. Density functional theory (DFT) results show that common defects can readily occur and produce an energetic profile that agrees well with the experimental results. The DFT results suggest that the formation of iodine interstitials is the initial process leading to degradation, responsible for the emergence of midgap states, and that defect engineering will play a key role in enhancing the optoelectronic properties of 2D HaPs in the future.

Although studies on non-tuberculous mycobacteria have increased in recent years because they cause a considerable proportion of infections, their cellulolytic system is still poorly studied. This study presents a characterization of the cellulolytic activities of environmental mycobacterial isolates derived from soil and water samples from the central region of Argentina, aimed to evaluate the conservation of the mechanism for the degradation of cellulose in this group of bacteria. The molecular and genomic identification revealed identity with Mycolicibacterium septicum. The endoglucanase and total cellulase activities were assessed both qualitatively and quantitatively and the optimal enzymatic conditions were characterized. A specific protein of around 56 kDa with cellulolytic activity was detected in a zymogram. Protein sequences possibly arising from a cellulase were identified by mass spectrometry-based shotgun proteomics. Results showed that M. septicum encodes for cellulose- and hemicellulose-related degrading enzymes, including at least an active β-1,4 endoglucanase enzyme that could be useful to improve its survival in the environment. Given the important health issues related to mycobacteria, the results of the present study may contribute to the knowledge of their cellulolytic system, which could be important for their ability to survive in many different types of environments., Competing Interests: Declaration of competing interest The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Chiral Cu(II) bisoxazolines have been shown to catalyze the coupling of acetyl-protected carbohydrates with N-silylated indoles to give the corresponding N-glycosides. Preliminary mechanistic experiments indicated that catalysis occurs through formation of a Cu-indolide complex with concomitant formation of TMS-OTf which together activate the sugar and deliver the indole nucleophile.

To prevent catfish idiopathic anaemia, diets fortified with iron have been adopted as a regular practice on commercial catfish farms to promote erythropoiesis. However, the effects of prolonged exposure of excess dietary iron on production performance and disease resistance for hybrid catfish (Ictalurus punctatus × I. furcatus) remains unknown. Four experimental diets were supplemented with ferrous monosulphate to provide 0, 500, 1000, and 1500 mg of iron per kg of diet. Groups of 16 hybrid catfish juveniles (~22.4 g) were stocked in each of 20, 110-L aquaria (n = 5), and experimental diets were offered to the fish to apparent satiation for 12 weeks. At the end of the study, production performance, survival, condition indices, as well as protein and iron retention were unaffected by the dietary treatments. Blood haematocrit and the iron concentration in the whole-body presented a linear increase with the increasing the dietary iron. The remaining fish from the feeding trial was challenged with Edwardsiella ictaluri. Mortality was mainly observed for the dietary groups treated with iron supplemented diets. The results for this study suggest that iron supplementation beyond the required levels does affect the blood production, and it may increase their susceptibility to E. ictaluri infection., (© 2023 John Wiley & Sons Ltd.)

The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity ( K b ) and soil water content ( θ ). In fine-textured soils, the conversion of K b to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the K b - θ relationship was determined for temperature ( T ) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). K b is influenced by T in both soils with contrasting T - K b relationships. The measured data were fitted using a linear function θ = a Kb + b with temperature-dependent coefficients a and b . The slope, a ( T ), and intercept, b ( T ), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm 3 cm -3 , which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm 3 cm -3 ). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites.