Plasma Hsp90 levels in patients with systemic sclerosis and relation to lung and skin involvement: a cross-sectional and longitudinal study

TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding

Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

Sexual function and pelvic floor function in men with systemic sclerosis compared to healthy controls: a cross-sectional study

OBJECTIVES

This cross-sectional study aimed to compare the sexual function (SF) and pelvic floor function of men with systemic sclerosis (SSc) with age-matched healthy controls (HC) and to identify the implications of clinical features on SF.

MATERIAL AND METHOD

Twenty SSc males and 20 HC aged 18-70 years completed eleven questionnaires assessing SF [International Index of Erectile Function (IIEF), Male Sexual Health Questionnaire (MSHQ)]; sexual quality of life: Sexual Quality of Life Questionnaire-Male (SQoL-M); pelvic floor function: Pelvic Floor Impact Questionnaire-Short Form 7 (PFIQ-7), fatigue, depression, physical fitness, functional disability, and quality of life. Clinical data were collected.

RESULTS

Significantly worse SF was observed in patients (median IIEF erectile function 12 in SSc versus 29 in HC, p < 0.001), with 70% reporting erectile dysfunction (ED) compared to 15% in HC. However, no significant difference was observed regarding pelvic floor function (median PFIQ7 8.8 in SSc versus 7.0 in HC, p = 0.141). Impaired SF was associated with higher disease activity, increased systemic inflammation, more pronounced fatigue, reduced physical fitness, severe depression, impaired overall quality of life, dyspepsia, and arthralgias (p < 0.05 for all).

CONCLUSIONS

Sexual dysfunction is highly prevalent in our SSc patients, whereas pelvic floor dysfunction is unlikely to be associated with these problems.

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Computational pathology: A survey review and the way forward

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

Label-free and liquid state SERS detection of multi-scaled bioanalytes via light-induced pinpoint colloidal assembly

Surface-enhanced Raman scattering (SERS) has been extensively applied to detect complex analytes due to its ability to enhance the fingerprint signals of molecules around nanostructured metallic surfaces. Thus, it is essential to design SERS-active nanostructures with abundant electromagnetic hotspots in a probed volume according to the dimensions of the analytes, as the analytes must be located in their hotspots for maximum signal enhancement. Herein, we demonstrate a simple method for detecting robust SERS signals from multi-scaled bioanalytes, regardless of their dimensions in the liquid state, through a photothermally driven co-assembly with colloidal plasmonic nanoparticles as signal enhancers. Under resonant light illumination, plasmonic nanoparticles and analytes in the solution quickly assemble at the focused surface area by convective movements induced by the photothermal heating of the plasmonic nanoparticles without any surface modification. Such collective assemblies of plasmonic nanoparticles and analytes were optimized by varying the optical density and surface charge of the nanoparticles, the viscosity of the solvent, and the light illumination time to maximize the SERS signals. Using these light-induced co-assemblies, the intrinsic SERS signals of small biomolecules can be detected down to nanomolar concentrations based on their fingerprint spectra. Furthermore, large-sized biomarkers, such as viruses and exosomes, were successfully detected without labels, and the complexity of the collected spectra was statistically analyzed using t-distributed stochastic neighbor embedding combined with support vector machine (t-SNE + SVM). The proposed method is expected to provide a robust and convenient method to sensitively detect biologically and environmentally relevant analytes at multiple scales in liquid samples.

Physical exercise-related manifestations of long COVID: A systematic review and meta-analysis

Objective

This study aims to systematically assess physical exercise-related symptoms of post-acute sequelae of SARS-CoV-2 infection (PASC or long COVID) in coronavirus disease 2019 (COVID-19) survivors.

Methods

Eight databases were systematically searched on March 03, 2024. Original studies that compared physical exercise-related parameters measured by exercise testing between COVID-19 survivors who recovered from SARS-CoV-2 infection over 3 months and non-COVID-19 controls were included. A random-effects model was utilized to determine the mean differences (MDs) or standardized MDs in the meta-analysis.

Results

A total of 40 studies with 6241 COVID-19 survivors were included. The 6-min walk test, maximal oxygen consumption (VO2max), and anaerobic threshold were impaired in COVID-19 survivors 3 months post-infection compared with non-COVID-19 controls in exercise testing, while VO2 were comparable between the two groups at rest. In contrast, no differences were observed in SpO2, heart rate, blood pressure, fatigue, and dyspnea between COVID-19 survivors and non-COVID-19 controls in exercise testing.

Conclusion

The findings suggest an underestimation of the manifestations of PASC. COVID-19 survivors also harbor physical exercise-related symptoms of PASC that can be determined by the exercise testing and are distinct from those observed at rest. Exercise testing should be included while evaluating the symptoms of PASC in COVID-19 survivors.

Gate modulation of barrier height of unipolar vertically stacked monolayer ReS2/MoS2 heterojunction

This study investigates vertically stacked CVD grown ReS2/MoS2 unipolar heterostructure device as Field Effect Transistor (FET) device wherein ReS2 on top acts as drain and MoS2 at bottom acts as source. The electrical measurements of ReS2/MoS2 FET device were carried out and variation in Ids (drain current) Vs Vds (drain voltage) for different Vgs (gate voltage) revealing the n-type device characteristics. Furthermore, the threshold voltage was calculated at the gate bias voltage corresponding to maximum transconductance (gm) value which is ~ 12 V. The mobility of the proposed ReS2/MoS2 heterojunction FET device was calculated as 60.97 cm2 V-1 s-1. The band structure of the fabricated vDW heterostructure was extracted utilizing ultraviolet photoelectron spectroscopy and the UV-visible spectroscopy revealing the formation of 2D electron gas (2DEG) at the ReS2/MoS2 interface which explains the high carrier mobility of the fabricated FET. The field effect behavior is studied by the modulation of the barrier height across heterojunction and detailed explanation is presented in terms of the charge transport across the heterojunction.

A systems serology approach to identifying key antibody correlates of protection from cerebral malaria in Malawian children

BACKGROUND

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) proteins are expressed on the surface of infected erythrocytes, mediating parasite sequestration in the vasculature. PfEMP1 is a major target of protective antibodies, but the features of the antibody response are poorly defined.

METHODS

In Malawian children with cerebral or uncomplicated malaria, we characterized the antibody response to 39 recombinant PfEMP1 Duffy binding like (DBL) domains or cysteine-rich interdomain regions (CIDRs) in detail, including measures of antibody classes, subclasses, and engagement with Fcγ receptors and complement. Using elastic net regularized logistic regression, we identified a combination of seven antibody targets and Fc features that best distinguished between children with cerebral and uncomplicated malaria. To confirm the role of the selected targets and Fc features, we measured antibody-dependent neutrophil and THP-1 cell phagocytosis of intercellular adhesion molecule-1 (ICAM-1) and endothelial protein C (EPCR) co-binding infected erythrocytes.

RESULTS

The selected features distinguished between children with cerebral and uncomplicated malaria with 87% accuracy (median, 80-96% interquartile range) and included antibody to well-characterized DBLβ3 domains and a less well-characterized CIDRγ12 domain. The abilities of antibodies to engage C1q and FcγRIIIb, rather than levels of IgG, correlated with protection. In line with a role of FcγRIIIb binding antibodies to DBLβ3 domains, antibody-dependent neutrophil phagocytosis of ICAM-1 and EPCR co-binding IE was higher in uncomplicated malaria (15% median, 8-38% interquartile range) compared to cerebral malaria (7%, 30-15%, p < 0.001).

CONCLUSIONS

Antibodies associated with protection from cerebral malaria target a subset of PfEMP1 domains. The Fc features of protective antibody response include engagement of FcγRIIIb and C1q, and ability to induce antibody-dependent neutrophil phagocytosis of infected erythrocytes. Identifying the targets and Fc features of protective immunity could facilitate the development of PfEMP1-based therapeutics for cerebral malaria.

Finite element modeling of plasmonic resonances in photothermal gold nanoparticles embedded in cells

The use of plasmonic nanoparticles in performing photothermal treatments in cancer cells requires a full knowledge about their optical properties. The surface plasmon resonance is easily foreseen and measurable in colloidal suspensions, however it can be strongly modified when located inside cells. Assessing the optical behavior of plasmonic nanoparticles in cells is essential for an efficient and controlled treatment. This requires the combination of experimental data and computational models to understand the mechanisms that cause the change in their optical response. In this work, we investigate the plasmonic response of Au nanospheres (AuNSs) internalized into cancer cells (MCF-7). Experimental data are compared to the simulations provided by a 3D model based on a finite element method. We demonstrate the impact of physical parameters such as the type of NS assembly, the surrounding medium and the interparticle gap, in the photothermal efficiency of AuNSs. Results open the avenue to predict, by numerical calculations, the optical properties of plasmonic nanoparticles inside cells to minimize treatment costs and times in photothermal therapies.

Spatiotemporal modeling of the potential impact of climate change on shifts in bioclimatic zones in Morocco

This study aims to contribute to the understanding of the impact of climate change on bioclimatic zones in Morocco, providing insights into potential shifts and emphasizing the need for adaptation measures to protect vulnerable species and ecosystems. To achieve this, we utilized eight general circulation models (GCMs) to simulate climate conditions under two representative concentration scenarios (RCP4.5 and RCP8.5) for two future time points (2050 and 2070). The modeling of bioclimatic zone shifts was accomplished through the implementation of the random forest (RF) algorithm. Our findings indicate that the subhumid and humid areas are expected to experience the most significant shifts, particularly toward the semi-arid zone. Shifts from subhumid to semi-arid were the most pronounced, ranging from 17.91% (RCP8.5 in 2070) to 25.68% (RCP8.5 in 2050), while shifts from humid to semi-arid ranged from 10.16% (RCP4.5 in 2050) to 22.27% (RCP8.5 in 2070). The Saharan and arid zones are expected to be the least affected, with less than 1% and 11% of their original extent expected to change, respectively. Moreover, our results suggest that forest species such as Atlas cedar and oaks are among the most vulnerable to these shifts. Overall, this study highlights the inevitability of climate change's impact on Moroccan ecosystems and provides a basis for adaptation measures, especially considering the species adapted to the bioclimatic conditions that will dominate the respective affected regions.

Demonstration of in-vivo simultaneous 3D imaging with 18F-FDG and Na131I using Compton-PET system

Simultaneous imaging of the SPECT tracer 131I and PET tracer 18F is important in the diagnosis of high- and low-grade thyroid cancers because high-grade thyroid cancers have high 18F-FDG and low 131I uptake, while low-grade thyroid cancers have high 131I and low 18F-FDG uptake. In this study, Na131I and 18F-FDG were simultaneously imaged using the Compton-PET system, in vivo. The angular resolution and sensitivity of the Compton camera with 356 keV gamma ray measured using a 133Ba point source were 12.3° and 2 × 10-5, respectively. The spatial resolution and sensitivity of PET were measured with a 22Na point source. The transaxial and axial spatial resolutions of the PET at the center of the FOV were 1.15 mm and 2.04 mm, respectively. Its sensitivity was 1.2 × 10-4. In-vivo images of the 18F and 131I isotopes were simultaneously acquired from mice. These showed that 18F-FDG was active in the heart, brown fat, and brain, while Na131I was active in the thyroid, stomach, and bladder. Artifacts were found in the Compton camera images when the activity of 131I was much lower than that of 18F. This study demonstrates the potential of simultaneous clinical imaging of 18F and 131I.

Human Tooth Crack Image Analysis with Multiple Deep Learning Approaches

Tooth cracks, one of the most common dental diseases, can result in the tooth falling apart without prompt treatment; dentists also have difficulty locating cracks, even with X-ray imaging. Indocyanine green (ICG) assisted near-infrared fluorescence (NIRF) dental imaging technique can solve this problem due to the deep penetration of NIR light and the excellent fluorescence characteristics of ICG. This study extracted 593 human cracked tooth images and 601 non-cracked tooth images from NIR imaging videos. Multiple imaging analysis methods such as classification, object detection, and super-resolution were applied to the dataset for cracked image analysis. Our results showed that machine learning methods could help analyze tooth crack efficiently: the tooth images with cracks and without cracks could be well classified with the pre-trained residual network and squeezenet1_1 models, with a classification accuracy of 88.2% and 94.25%, respectively; the single shot multi-box detector (SSD) was able to recognize cracks, even if the input image was at a different size from the original cracked image; the super-resolution (SR) model, SR-generative adversarial network demonstrated enhanced resolution of crack images using high-resolution concrete crack images as the training dataset. Overall, deep learning model-assisted human crack analysis improves crack identification; the combination of our NIR dental imaging system and deep learning models has the potential to assist dentists in crack diagnosis.

Zn-Al and Mg-Al layered double hydroxide nanoparticles improved primary and secondary metabolism of geranium plants

Layer double hydroxide (LDH) nanoparticles (NPs) have been applied to enhance plant growth and productivity. However, their effects on carbon and nitrogen metabolism of aromatic plants, are not well understood. Therefore, we investigated the impact of foliar application of Zn-Al LDH and Mg-Al LDH NPs (10 ppm) on the growth and metabolism of geranium plants. Zn-Al LDH and Mg-Al LDH NPs significantly increased the dry biomass, photosynthetic pigment, and Zn and Mg uptake by treated plants. These increases were consistent with increased primary metabolism such as soluble sugars and their metabolic enzymes (invertase and amylase). The supply of high sugar levels induced TCA organic accumulation, providing a pathway for amino acid biosynthesis. Among amino acids, proline level and its biosynthetic enzymes such as pyrroline-5-carboxylate reductase (P5CR), ornithine aminotransferase (OAT), and pyrroline-5-carboxylate synthetase (P5CS), glutamine synthetase (GS), and arginase were increased. Increased primary metabolites can then be channeled into secondary metabolic pathways, leading to higher levels of secondary metabolites including tocopherols, phenolics, and flavonoids. These observed increases in primary and secondary metabolites also improve the biological value of geranium plants. Overall, our research highlights the potential of Zn-Al LDH and Mg-Al LDH NPs as elicitors to enhance metabolism in geranium plants, thereby improving their growth bioactivity.

PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms

The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold. One hundred five radiomic features were extracted from each region of interest (ROI). Multiple feature selection methods, including Minimum Redundancy Maximum Relevance (MRMR), Recursive Feature Elimination (RFE), and Boruta, and multiple classifiers, including Multilayer Perceptron (MLP), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF), were employed. Synthetic Minority Oversampling Technique (SMOTE) was also used to determine if it boosts the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Our results indicated that the combination of SMOTE, IT (with 45% threshold), RFE feature selection and LR classifier showed the best performance (AUC = 0.93, ACC = 0.84, SEN = 0.85, SPE = 0.84) followed by SMOTE, FCM segmentation, MRMR feature selection, and LR classifier (AUC = 0.92, ACC = 0.87, SEN = 1, SPE = 0.84). The highest ACC belonged to the IT segmentation (with 45 and 50% thresholds) alongside Boruta feature selection and the NB classifier without SMOTE (ACC = 0.9, AUC = 0.78 and 0.76, SEN = 0.7, and SPE = 0.94, respectively). Our results indicate that selection of appropriate segmentation method and machine learning algorithm may be helpful in successful prediction of LVI in patients with NSCLC with high accuracy using PET radiomics analysis.

Factors affecting the survival of prediabetic patients: comparison of Cox proportional hazards model and random survival forest method

BACKGROUND

The worldwide prevalence of type 2 diabetes mellitus in adults is experiencing a rapid increase. This study aimed to identify the factors affecting the survival of prediabetic patients using a comparison of the Cox proportional hazards model (CPH) and the Random survival forest (RSF).

METHOD

This prospective cohort study was performed on 746 prediabetics in southwest Iran. The demographic, lifestyle, and clinical data of the participants were recorded. The CPH and RSF models were used to determine the patients' survival. Furthermore, the concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curve were employed to compare the performance of the Cox proportional hazards (CPH) model and the random survival forest (RSF) model.

RESULTS

The 5-year cumulative T2DM incidence was 12.73%. Based on the results of the CPH model, NAFLD (HR = 1.74, 95% CI: 1.06, 2.85), FBS (HR = 1.008, 95% CI: 1.005, 1.012) and increased abdominal fat (HR = 1.02, 95% CI: 1.01, 1.04) were directly associated with diabetes occurrence in prediabetic patients. The RSF model suggests that factors including FBS, waist circumference, depression, NAFLD, afternoon sleep, and female gender are the most important variables that predict diabetes. The C-index indicated that the RSF model has a higher percentage of agreement than the CPH model, and in the weighted Brier Score index, the RSF model had less error than the Kaplan-Meier and CPH model.

CONCLUSION

Our findings show that the incidence of diabetes was alarmingly high in Iran. The results suggested that several demographic and clinical factors are associated with diabetes occurrence in prediabetic patients. The high-risk population needs special measures for screening and care programs.

Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs

BACKGROUND/OBJECTIVES

Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening potential arising from signals persisting despite training and/or ambiguous signals such as from biomarker overlap or high comorbidity. The study aimed to explore the potential to detect clinically useful incidental ocular biomarkers by screening fundus photographs of hypertensive adults using diabetic deep learning algorithms.

SUBJECTS/METHODS

Patients referred for treatment-resistant hypertension were imaged at a hospital unit in Perth, Australia, between 2016 and 2022. The same 45° colour fundus photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. Two expert retinal specialists graded all false-positive results for diabetic retinopathy in non-diabetic participants.

RESULTS

Of the 29 non-diabetic participants misclassified as positive for diabetic retinopathy, 28 (97%) had clinically useful retinal biomarkers. The models designed to screen for fewer diseases captured more incidental disease. All three algorithms showed a positive correlation between severity of hypertensive retinopathy and misclassified diabetic retinopathy.

CONCLUSIONS

The results suggest that diabetic deep learning models may be responsive to hypertensive and other clinically useful retinal biomarkers within an at-risk, hypertensive cohort. Observing that models trained for fewer diseases captured more incidental pathology increases confidence in signalling hypotheses aligned with using self-supervised learning to develop autonomous comprehensive screening. Meanwhile, non-referable and false-positive outputs of other deep learning screening models could be explored for immediate clinical use in other populations.

Influence of waning immunity on vaccination decision-making: A multi-strain epidemic model with an evolutionary approach analyzing cost and efficacy

In this research, we introduce a comprehensive epidemiological model that accounts for multiple strains of an infectious disease and two distinct vaccination options. Vaccination stands out as the most effective means to prevent and manage infectious diseases. However, when there are various vaccines available, each with its costs and effectiveness, the decision-making process for individuals becomes paramount. Furthermore, the factor of waning immunity following vaccination also plays a significant role in influencing these choices. To understand how individuals make decisions in the context of multiple strains and waning immunity, we employ a behavioral model, allowing an epidemiological model to be coupled with the dynamics of a decision-making process. Individuals base their choice of vaccination on factors such as the total number of infected individuals and the cost-effectiveness of the vaccine. Our findings indicate that as waning immunity increases, people tend to prioritize vaccines with higher costs and greater efficacy. Moreover, when more contagious strains are present, the equilibrium in vaccine adoption is reached more rapidly. Finally, we delve into the social dilemma inherent in our model by quantifying the social efficiency deficit (SED) under various parameter combinations.

Gastrointestinal digestion of olive leaf waste from olive mill production chain in a simulator of the gastrointestinal tract

BACKGROUND

Currently, olive leaves are considered waste, although their high phenolic content makes them a source of antioxidants that could be used directly. The aim of this work was to study the behavior of phenolic compounds contained in olive leaf from the olive mill production chain during their gastrointestinal digestion.

RESULTS

Phenolic compounds in the gastric digestion analyzed by high-performance liquid chromatography-diode array detection-mass spectrometry increased by 58% to 314.5% compared to the meal, while in the intestinal stage they ranged from 1.87 to 9.04 times higher. An increase of between 187% and 903% in bioaccessibility of phenolic compounds in the intestinal phase was observed, except for verbascoside. Furthermore, such compounds were fully bioavailable, except for apigenin-7-O-glucoside, which showed a bioavailability of 56%.

CONCLUSION

The current study showed that the concentration of oleuropein, apigegin-7-O-glucoside, hydroxytyrosol hexoside and hydroxytyrosol contained in olive leaf continued to be extracted during the gastrointestinal digestion process. Furthermore, the results obtained with respect to their bioaccessibility and bioavailability suggest a good disposition to pass into the bloodstream where they could exert beneficial effects. Therefore, these results are promising for olive leaf becoming a consumable by-product that could be directly ingested through a simple infusion. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Insilico assessment of hesperidin on SARS-CoV-2 main protease and RNA polymerase: Molecular docking and dynamics simulation approach

The present study uses molecular docking and dynamic simulations to evaluate the inhibitory effect of flavonoid glycosides-based compounds on coronavirus Main protease (Mpro) and RNA polymerase. The Molegro Virtual Docker (MVD) software is utilized to simulate and calculate the binding parameters of compounds with coronavirus. The docking results show that the selected herbal compounds are more effective than those of chemical compounds. It is also revealed that five herbal ligands and two chemical ligands have the best docking scores. Furthermore, a Molecular Dynamics (MD) simulation was conducted for Hesperidin, confirming docking results. Analysis based on different parameters such as Root-mean-square deviation (RMSD), Root mean square fluctuation (RMSF), Radius of gyration (Rg), Solvent accessibility surface area (SASA), and the total number of hydrogen bonds suggests that Hesperidin formed a stable complex with Mpro. Absorption, Distribution, Metabolism, Excretion, And Toxicity (ADMET) analysis was performed to compare Hesperidin and Grazoprevir as potential antiviral medicines, evaluating both herbal and chemical ligand results. According to the study, herbal compounds could be effective on coronavirus and are admissible candidates for developing potential operative anti-viral medicines. Hesperidin was found to be the most acceptable interaction. Grazoprevir is an encouraging candidate for drug development and clinical trials, with the potential to become a highly effective Mpro inhibitor. Compared to RNA polymerase, Mpro showed a greater affinity for bonding with Hesperidin. van der Waals and electrostatic energies dominated, creating a stable Hesperidin-Mpro and Hesperidin-RNA polymerase complex.

Fundamentals of Arthroscopic Surgery Training and beyond: a reinforcement learning exploration and benchmark

PURPOSE

This work presents FASTRL, a benchmark set of instrument manipulation tasks adapted to the domain of reinforcement learning and used in simulated surgical training. This benchmark enables and supports the design and training of human-centric reinforcement learning agents which assist and evaluate human trainees in surgical practice.

METHODS

Simulation tasks from the Fundamentals of Arthroscopic Surgery Training (FAST) program are adapted to the reinforcement learning setting for the purpose of training virtual agents that are capable of providing assistance and scoring to the surgical trainees. A skill performance assessment protocol is presented based on the trained virtual agents.

RESULTS

The proposed benchmark suite presents an API for training reinforcement learning agents in the context of arthroscopic skill training. The evaluation scheme based on both heuristic and learned reward functions robustly recovers the ground truth ranking on a diverse test set of human trajectories.

CONCLUSION

The presented benchmark enables the exploration of a novel reinforcement learning-based approach to skill performance assessment and in-procedure assistance for simulated surgical training scenarios. The evaluation protocol based on the learned reward model demonstrates potential for evaluating the performance of surgical trainees in simulation.

Highly Accurate and Precise Automated Cup-to-Disc Ratio Quantification for Glaucoma Screening

Objective

An enlarged cup-to-disc ratio (CDR) is a hallmark of glaucomatous optic neuropathy. Manual assessment of the CDR may be less accurate and more time-consuming than automated methods. Here, we sought to develop and validate a deep learning-based algorithm to automatically determine the CDR from fundus images.

Design

Algorithm development for estimating CDR using fundus data from a population-based observational study.

Participants

A total of 181 768 fundus images from the United Kingdom Biobank (UKBB), Drishti_GS, and EyePACS.

Methods

FastAI and PyTorch libraries were used to train a convolutional neural network-based model on fundus images from the UKBB. Models were constructed to determine image gradability (classification analysis) as well as to estimate CDR (regression analysis). The best-performing model was then validated for use in glaucoma screening using a multiethnic dataset from EyePACS and Drishti_GS.

Main Outcome Measures

The area under the receiver operating characteristic curve and coefficient of determination.

Results

Our gradability model vgg19_batch normalization (bn) achieved an accuracy of 97.13% on a validation set of 16 045 images, with 99.26% precision and area under the receiver operating characteristic curve of 96.56%. Using regression analysis, our best-performing model (trained on the vgg19_bn architecture) attained a coefficient of determination of 0.8514 (95% confidence interval [CI]: 0.8459-0.8568), while the mean squared error was 0.0050 (95% CI: 0.0048-0.0051) and mean absolute error was 0.0551 (95% CI: 0.0543-0.0559) on a validation set of 12 183 images for determining CDR. The regression point was converted into classification metrics using a tolerance of 0.2 for 20 classes; the classification metrics achieved an accuracy of 99.20%. The EyePACS dataset (98 172 healthy, 3270 glaucoma) was then used to externally validate the model for glaucoma classification, with an accuracy, sensitivity, and specificity of 82.49%, 72.02%, and 82.83%, respectively.

Conclusions

Our models were precise in determining image gradability and estimating CDR. Although our artificial intelligence-derived CDR estimates achieve high accuracy, the CDR threshold for glaucoma screening will vary depending on other clinical parameters.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Early neonatal mortality in Ethiopia from 2000 to 2019: an analysis of trends and a multivariate decomposition analysis of Ethiopian demographic and health survey

BACKGROUND

Early neonatal deaths, occurring within the first six days of life, remain a critical public health challenge. Understanding the trends and factors associated with this issue is crucial for designing effective interventions and achieving global health goals. This study aims to examine the trends in early neonatal mortality in Ethiopia and identify the key factors associated with changes in early neonatal mortality over time.

METHODS

This study utilized five consecutive Ethiopian Demographic and Health Survey datasets from 2000 to 2019. To investigate the trends and identify factors influencing changes in early neonatal mortality over time, conducted a trend analysis and a logit-based multivariate decomposition analysis. Data management and analyses were performed using STATA version 17/MP software. All analyses were weighted to account for sampling probabilities and non-response. Statistical significance was determined at a two-sided p-value threshold of less than 0.05.

RESULT

The analysis included a total of 12,260 weighted women from the 2000 survey and 5,527 weighted women from the 2019 survey. Over the study period, there was an overall downward trend in early neonatal mortality, decreasing from 34 deaths per 1000 live births in 2000 to 27 deaths per 1000 live births in 2019. The annual rate of reduction was estimated to be 1.03%. Approximately 45% of the observed decline in early neonatal mortality rate can be attributed to changes in population characteristics or endowments (E) during the study period. Factors such as the mother's age, maternal education, marital status, preceding birth interval, types of pregnancy, and the sex of the child significantly contributed to the compositional change in the early neonatal mortality rate.

CONCLUSION

Over the past two decades, Ethiopia has seen a modest decline in early neonatal mortality, but this progress falls short of the Sustainable Development Goal (SDGs) targets. To achieve the SDGs, the Ministry of Health and its partners should intensify efforts to reduce early neonatal mortality. Strategies like preventing early/late pregnancies, promoting appropriate marriage timing, and prioritizing education could help further reduce early neonatal deaths. Further research is also needed to explore the factors driving this issue.

Online stimulation of the prefrontal cortex during practice increases motor variability and modulates later cognitive transfer: a randomized, double-blinded and sham-controlled tDCS study

The benefits of learning a motor skill extend to improved task-specific cognitive abilities. The mechanistic underpinnings of this motor-cognition relationship potentially rely on overlapping neural resources involved in both processes, an assumption lacking causal evidence. We hypothesize that interfering with prefrontal networks would inhibit concurrent motor skill performance, long-term learning and associated cognitive functions dependent on similar networks (transfer). We conducted a randomised, double-blinded, sham-controlled brain stimulation study using transcranial direct current stimulation (tDCS) in young adults spanning over three weeks to assess the role of the prefrontal regions in learning a complex balance task and long-term cognitive performance. Balance training combined with active tDCS led to higher performance variability in the trained task as compared to the sham group, impacting the process of learning a complex task without affecting the learning rate. Furthermore, active tDCS also positively influenced performance in untrained motor and cognitive tasks. The findings of this study help ascertaining the networks directly involved in learning a complex motor task and its implications on cognitive function. Hence, opening up the possibility of harnessing the observed frontal networks involved in resource mobilization in instances of aging, brain lesion/injury or dysfunction.

Rational design of nanoscale stabilized oxide catalysts for OER with OC22

The efficiency of H2 production via water electrolysis is limited by the sluggish oxygen evolution reaction (OER). As such, significant emphasis has been placed upon improving the rate of OER through the anode catalyst. More recently, the Open Catalyst 2022 (OC22) framework has provided a large dataset of density functional theory (DFT) calculations for OER intermediates on the surfaces of oxides. When coupled with state-of-the-art graph neural network models, total energy predictions can be achieved with a mean absolute error as low as 0.22 eV. In this work, we interpolated a database of the total energy predictions for all slabs and OER surface intermediates for 4119 oxide materials in the original OC22 dataset using pre-trained models from the OC22 framework. This database includes all terminations of all facets up to a maximum Miller index of 1. To demonstrate the full utility of this database, we constructed a flexible screening framework to identify viable candidate anode catalysts for OER under varying reaction conditions for bulk, surface, and nanoscale Pourbaix stability as well as material cost, overpotential, and metastability. From our assessment, we were able to identify 122 and 68 viable candidates for OER under the bulk and nanoscale regime, respectively.

An approach to integrated management of diabetes in tuberculosis patients: Availability and readiness of the health facilities of Bangladesh

BACKGROUND

Comorbidity of diabetes mellitus and tuberculosis (TB) is a major public health concern in low- and middle-income countries including Bangladesh. An integrated approach is required for adequate management of diabetes mellitus and TB. The objective of the present study was to investigate the availability and readiness of the TB care centers of Bangladesh toward diabetic patients' management.

METHODS

The present study was conducted based on existing data obtained from the Bangladesh Health Facility Survey (BHFS) 2017. Data collected from a total of 303 facilities providing TB services were retrieved. The outcome variables of the present study were availability and readiness of the TB health facilities for providing diabetes mellitus service. Readiness was measured for four domains: staff and guidelines, equipment, diagnostic facility and basic medicine. The independent variables were: facility level, management authority and location of the facility. Binary and multiple logistic regression models were constructed for both the outcome variables (availability and readiness) to find out their predictors.

RESULTS

Services for diabetes mellitus were available in 68% of the TB facilities while high readiness was present in 36% of the facilities. For domain-specific readiness index, readiness for the domains of staff and guidelines, equipment, diagnostic facility and basic medicine was reported in 46%, 96%, 38% and 25% facilities respectively. In the logistic regression model, availability of diabetes mellitus services was better in primary level (aOR 2.62, 95% CI 1.78-4.77) and secondary level (aOR 3.26, 95% CI 1.82-9.05) facilities than community facilities. Similarly, readiness of diabetes mellitus care was also better in these facilities (aOR 2.55, 95% CI 1.05-4.71 for primary and aOR 2.75, 95% CI 1.80-4.32 for secondary facilities). Besides, private TB facilities had better availability (aOR 2.84, 95% CI 1.75-5.89) and readiness (aOR 2.52, 95% CI 1.32-4.29) for diabetes mellitus care.

CONCLUSION

Availability and readiness for providing diabetes mellitus services in TB care providing facilities in Bangladesh is inadequate.

Premature aging effects on COVID-19 pathogenesis: new insights from mouse models

Aging is identified as a significant risk factor for severe coronavirus disease-2019 (COVID-19), often resulting in profound lung damage and mortality. Yet, the biological relationship between aging, aging-related comorbidities, and COVID-19 remains incompletely understood. This study aimed to elucidate the age-related COVID19 pathogenesis using an Hutchinson-Gilford progeria syndrome (HGPS) mouse model, a premature aging disease model, with humanized ACE2 receptors. Pathological features were compared between young, aged, and HGPS hACE2 mice following SARS-CoV-2 challenge. We demonstrated that young mice display robust interferon response and antiviral activity, whereas this response is attenuated in aged mice. Viral infection in aged mice results in severe respiratory tract hemorrhage, likely contributing a higher mortality rate. In contrast, HGPS hACE2 mice exhibit milder disease manifestations characterized by minor immune cell infiltration and dysregulation of multiple metabolic processes. Comprehensive transcriptome analysis revealed both shared and unique gene expression dynamics among different mouse groups. Collectively, our studies evaluated the impact of SARS-CoV-2 infection on progeroid syndromes using a HGPS hACE2 mouse model, which holds promise as a useful tool for investigating COVID-19 pathogenesis in individuals with premature aging.

Global transcriptome analysis identifies critical functional modules associated with multiple abiotic stress responses in microalgae Chromochloris zofingiensis

In the current study, systems biology approach was applied to get a deep insight regarding the regulatory mechanisms of Chromochloris zofingiensis under overall stress conditions. Meta-analysis was performed using p-values combination of differentially expressed genes. To identify the informative models related to stress conditions, two distinct weighted gene co-expression networks were constructed and preservation analyses were performed using medianRankand Zsummary algorithms. Moreover, functional enrichment analysis of non-preserved modules was performed to shed light on the biological performance of underlying genes in the non-preserved modules. In the next step, the gene regulatory networks between top hub genes of non-preserved modules and transcription factors were inferred using ensemble of trees algorithm. Results showed that the power of beta = 7 was the best soft-thresholding value to ensure a scale-free network, leading to the determination of 12 co-expression modules with an average size of 128 genes. Preservation analysis showed that the connectivity pattern of the six modules including the blue, black, yellow, pink, greenyellow, and turquoise changed during stress condition which defined as non-preserved modules. Examples of enriched pathways in non-preserved modules were Oxidative phosphorylation", "Vitamin B6 metabolism", and "Arachidonic acid metabolism". Constructed regulatory network between identified TFs and top hub genes of non-preserved module such as Cz06g10250, Cz03g12130 showed that some specific TFs such as C3H and SQUAMOSA promoter binding protein (SBP) specifically regulates the specific hubs. The current findings add substantially to our understanding of the stress responsive underlying mechanism of C. zofingiensis for future studies and metabolite production programs.

Soft Fibrous Syringe Architecture for Electricity-Free and Motorless Control of Flexible Robotic Systems

Flexible robotic systems (FRSs) and wearable user interfaces (WUIs) have been widely used in medical fields, offering lower infection risk and shorter recovery, and supporting amiable human-machine interactions (HMIs). Recently, soft electric, thermal, magnetic, and fluidic actuators with enhanced safety and compliance have innovatively boosted the use of FRSs and WUIs across many sectors. Among them, soft hydraulic actuators offer great speed, low noise, and high force density. However, they currently require bulky electric motors/pumps, pistons, valves, rigid accessories, and complex controllers, which inherently result in high cost, low adaptation, and complex setups. This paper introduces a novel soft fibrous syringe architecture (SFSA) consisting of two or more hydraulically connected soft artificial muscles that enable electricity-free actuation, motorless control, and built-in sensing ability for use in FRSs and WUIs. Its capabilities are experimentally demonstrated with various robotic applications including teleoperated flexible catheters, cable-driven continuum robotic arms, and WUIs. In addition, its sensing abilities to detect passive and active touch, surface texture, and object stiffness are also proven. These excellent results demonstrate a high feasibility of using a current-free and motor-less control approach for the FRSs and WUIs, enabling new methods of sensing and actuation across the robotic field.

Diversity of bacteria of the genus Sphingomonas associated with sugarcane (Saccharum spp.) culm apoplast fluid and their agrotechnological potential

In sugarcane, sequences related to the genus Sphingomonas have been widely detected by microbiome studies. In this work, the presence of bacteria of this genus was confirmed using culture-dependent and independent techniques. A collection of thirty isolates was obtained using semispecific cultivation conditions, and a specific PCR assay was applied to help confirm the isolates as belonging to the genus. A series of laboratory evaluations were carried out to identify potential properties among the isolates in the collection, which consequently allowed the identification of some most promising isolates for the development of new agricultural bioinputs. In a separate analysis, the culture-independent fluorescence in situ hybridization (FISH) methodology was applied to demonstrate the natural occurrence of Sphingomonas in different organs and tissues of sugarcane. The results showed the presence of bacteria of the genus in the spaces between cells (apoplast) of the culm parenchyma, in vessels in the region of the leaf vein, on the adaxial surface of the leaf blade, and on the root surface, sometimes close to the base of root hairs, which suggests extensive colonization on the host plant. In summary, the present study corroborates previous metagenomic amplicon sequencing results that indicated a high occurrence of Sphingomonas associated with sugarcane. This is the first study that uses approaches other than amplicon sequencing to confirm the occurrence of the genus in sugarcane and, at the same time, demonstrates potentially beneficial activities to be explored by sugarcane cultivation.

Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas

The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features or convolutional neural networks (CNNs). We aimed to build and assess a combined radiomics and CNN non-invasive pLGG molecular status identification model. This retrospective study used the tumor regions, manually segmented from T2-FLAIR MR images, of 336 patients treated for pLGG between 1999 and 2018. We designed a CNN and Random Forest radiomics model, along with a model relying on a combination of CNN and radiomic features, to predict the genetic status of pLGG. Additionally, we investigated whether CNNs could predict radiomic feature values from MR images. The combined model (mean AUC: 0.824) outperformed the radiomics model (0.802) and CNN (0.764). The differences in model performance were statistically significant (p-values < 0.05). The CNN was able to learn predictive radiomic features such as surface-to-volume ratio (average correlation: 0.864), and difference matrix dependence non-uniformity normalized (0.924) well but was unable to learn others such as run-length matrix variance (- 0.017) and non-uniformity normalized (- 0.042). Our results show that a model relying on both CNN and radiomic-based features performs better than either approach separately in differentiating the genetic status of pLGGs, and that CNNs are unable to express all handcrafted features.

Spatial resolution and optical sensitivity in the compound eyes of two common European wasps, Vespula germanica and Vespula vulgaris

Vespula germanica and Vespula vulgaris are two common European wasps that have ecological and economic importance as a result of their artificial introduction into many different countries and environments. Their success has undoubtedly been aided by their capacity for visually guided hunting, foraging, learning and using visual cues in the context of homing and navigation. However, the visual systems of V. germanica and V. vulgaris have not received any deep attention. We used electrophysiology, together with optical and anatomical techniques, to measure the spatial resolution and optical sensitivity of the compound eyes of both species. We found that both wasps have high anatomical spatial resolution with narrow interommatidial angles (Δϕ between 1.0 and 1.5 deg) and a distinct acute zone in the fronto-ventral part of the eye. These narrow interommatidial angles are matched to photoreceptors having narrow angular sensitivities (acute zone acceptance angles Δρ below 1.3 deg), indicating eyes of high spatial resolution that are well suited to their ecological needs. Additionally, we found that both species possess an optical sensitivity that is typical of other day-flying hymenopterans.

Nutritional and biofunctional characterizations of four novel edible aquatic plants of Bangladesh

Aquatic plants are a cheap and renewable biomass rich in bioactive and biofunctional compounds, holding valorization prospects for use in food and pharmaceuticals. Four commonly found edible aquatic plants in Bangladesh, namely red water lily (Nymphaea nouchali), white water lily (Nympheae alba), malancha (Alternanthera philoxeroides), and red seaweed (Gracilaria tenuistipitata), were compared in terms of proximate composition, bioactive compounds, antioxidant activity, mineral and heavy metal contents, and amino acid composition. The crude protein content was the highest in A. philoxeroids (26.96 %), followed by G. tenuistipitata (25.21 %), N. nouchali (25.14 %), and N. alba (23.54 %). The sequence of crude lipid content of four aquatic plants was A. philoxeroids (4.8 %) > N. nouchali (4.0 %) > G. tenuistipitata (3.4 %) > N. alba (2.4 %). The aquatic plants were rich in carbohydrates, with G. tenuistipitata having 37.02 %, significantly (P < 0.05) lower than N. alba (46.12 %), N. nouchali (45.73 %), and A. philoxeroids (42.88 %). The ash content in the studied plants varied between 14.63 % and 24.97 %. Substantial numbers of bioactive compounds were identified in these plants: 42 in N. alba, 41 in N. nouchali, 40 in A. philoxeroides, and 36 in G. tenuistipitata, as determined by GC-MS analysis. G. tenuistipitata showed the highest amount of total phenolic (121.05 ± 2.43 mg gallic acid equivalent/g) and flavonoid (128.03 ± 0.79 mg quercetin equivalent/g) content. The DPPH, hydrogen peroxide, and ferric reducing power assays showed the free radical scavenging ability increased in a dose dependent manner. These aquatic plants contained substantial amounts of minerals, namely Ca ranging from 42.05 ± 2.34 to 441.65 ± 4.67 mg/kg, K ranging from 80.15 ± 1.82 to 97.81 ± 1.74 mg/kg, and Na ranging from 41.16 ± 1.32 to 53.37 ± 1.64 mg/kg. The heavy metal contents of Cu, Ni, and Pb were 0.93 ± 0.06 to 1.25 ± 0.09 mg/kg, 0.44 ± 0.02 to 3.86 ± 0.56 mg/kg, and 0.22 ± 0.02 to 0.67 ± 0.05 mg/kg, respectively. Thirteen different amino acids were identified, with leucine, glycine, alanine, lysine, and phenylalanine dominating, and their contents varying by species. Therefore, regular consumption of these aquatic plants might be a healthy approach to addressing malnutrition and enhancing biofunctional activities.

Clinical utility of fMRI in evaluating of LSD effect on pain-related brain networks in healthy subjects

Objective

We aimed to evaluate the effect of Lysergic acid diethylamide (LSD) on the pain neural network (PNN) in healthy subjects using functional magnetic resonance imaging (fMRI).

Methods

Twenty healthy volunteers participated in a balanced-order crossover study, receiving intravenous administration of LSD and placebo in two fMRI scanning sessions. Brain regions associated with pain processing were analyzed by amplitude of low-frequency fluctuation (ALFF), independent component analysis (ICA), functional connectivity and dynamic casual modeling (DCM).

Results

ALFF analysis demonstrated that LSD effectively relieves pain due to modulation in the neural network associated with pain processing. ICA analysis showed more active voxels in anterior cingulate cortex (ACC), thalamus (THL)-left, THL-right, insula cortex (IC)-right, parietal operculum (PO)-left, PO-right and frontal pole (FP)-right in the placebo session than the LSD session. There were more active voxels in FP-left and IC-left in the LSD session compared to the placebo session. Functional brain connectivity was observed between THL-left and PO-right and between PO-left with FP-left, FP-right and IC-left in the placebo session. In the LSD session, functional connectivity of PO-left with FP-left and FP-right was observed. The effective connectivity between left anterior insula cortex (lAIC)-lAIC, lAIC-dorsolateral prefrontal cortex (dlPFC) and secondary somatosensory cortex (SII)-dlPFC were significantly different. Finally, the correlation between fMRI biomarkers and clinical pain criteria was calculated.

Conclusion

This study enhances our understanding of the LSD effect on the architecture and neural behavior of pain in healthy subjects and provides great promise for future research in the field of cognitive science and pharmacology.

Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction

Introduction

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics.

Methods

Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival.

Results

SNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R 2 = 5.1 %; p-value = 3.2 × 10-27) and clinical characteristics.

Conclusions

Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

Identification and quantification of micro-nano-plastics in polypropylene-bottled injections

Micro-nano-plastic (MNP) particles (p) in the environment can enter the human body and pose a potential threat to human health. However, it is unknown whether these substances are present in polypropylene (PP) plastic-bottled injections, which are used as high-frequency intravenous infusions to treat diseases. Therefore, the objective of this study was to identify and quantify insoluble MNP particles in 16 batches of injectable formulations within the validity period. Primarily, ethylene-propylene copolymer or P(E-P) micro-plastic (MP) particles (2-10 μm, 216 p/mL) were identified by micro-Raman spectroscopy, and nano-particles (<50 nm, 2.1 × 104 p/mL) similar to PP containing only carbon were detected by scanning electron microscopy-energy-dispersive X-ray spectroscopy (photoelectron). Furthermore, P(E-P) MP particles (1 × 103 to 1 × 105 ng/L) from the injections were enriched on the GF-B filter, and PP or P(E-P) nano-plastic (NP) particles (1 × 103 to 4 × 104 ng/L) enriched on the alumina film were detected by pyrolysis-gas chromatography/mass spectrometry. Finally, the total insoluble particles in injections were 6 × 104 to 1 × 107 p/mL (0.02-100 μm). Our findings are the first to identify and quantify MNPs in PP-bottled injections. Considering that they can enter the blood circulation, so whether cause disease remains to be investigated.

Kinematic network of joint motion provides insight on gait coordination: An observational study on Parkinson's disease

The analysis of gait kinematics requires to encode and collapse multidimensional information from multiple anatomical elements. In this study, we address this issue by analyzing the joints' coordination during gait, borrowing from the framework of network theory. We recruited twenty-three patients with Parkinson's disease and twenty-three matched controls that were recorded during linear gait using a stereophotogrammetric motion analysis system. The three-dimensional angular velocity of the joints was used to build a kinematic network for each participant, and both global (average whole-body synchronization) and nodal (individual joint synchronization, i.e., nodal strength) were extracted. By comparing the two groups, the results showed lower coordination in patients, both at global and nodal levels (neck, shoulders, elbows, and hips). Furthermore, the nodal strength of the left elbow and right hip in the patients, as well as the average joints' nodal strength were significantly correlated with the clinical motor condition and were predictive of it. Our study highlights the importance of integrating whole-body information in kinematic analyses and the advantages of using network theory. Finally, the identification of altered network properties of specific joints, and their relationship with the motor impairment in the patients, suggests a potential clinical relevance for our approach.

Existence of multiple transitions of the critical state due to anesthetics

Scale-free statistics of coordinated neuronal activity, suggesting a universal operating mechanism across spatio-temporal scales, have been proposed as a necessary condition of healthy resting-state brain activity. Recent studies have focused on anesthetic agents to induce distinct neural states in which consciousness is altered to understand the importance of critical dynamics. However, variation in experimental techniques, species, and anesthetics, have made comparisons across studies difficult. Here we conduct a survey of several common anesthetics (isoflurane, pentobarbital, ketamine) at multiple dosages, using calcium wide-field optical imaging of the mouse cortex. We show that while low-dose anesthesia largely preserves scale-free statistics, surgical plane anesthesia induces multiple dynamical modes, most of which do not maintain critical avalanche dynamics. Our findings indicate multiple pathways away from default critical dynamics associated with quiet wakefulness, not only reflecting differences between these common anesthetics but also showing significant variations in individual responses. This is suggestive of a non-trivial relationship between criticality and the underlying state of the subject.

Histone deacetylases: potential therapeutic targets for idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease of unknown origin and the most common interstitial lung disease. However, therapeutic options for IPF are limited, and novel therapies are urgently needed. Histone deacetylases (HDACs) are enzymes that participate in balancing histone acetylation activity for chromatin remodeling and gene transcription regulation. Increasing evidence suggests that the HDAC family is linked to the development and progression of chronic fibrotic diseases, including IPF. This review aims to summarize available information on HDACs and related inhibitors and their potential applications in treating IPF. In the future, HDACs may serve as novel targets, which can aid in understanding the etiology of PF, and selective inhibition of single HDACs or disruption of HDAC genes may serve as a strategy for treating PF.

Feasibility of using diamond-like carbon films in total joint replacements: a review

Diamond-like Carbon (DLC) has been used as a coating material of choice for a variety of technological applications owing to its favorable bio-tribo-thermo-mechanical characteristics. Here, the possibility of bringing DLC into orthopedic joint implants is examined. With ever increasing number of patients suffering from osteoarthritis as well as with the ingress of the osteoarthritic joints' malaise into younger and more active demographics, there is a pressing need to augment the performance and integrity of conventional total joint replacements (TJRs). Contemporary joint replacement devices use metal-on-polymer articulations to restore function to worn, damaged or diseased cartilage. The wear of polymeric components has been addressed using crosslinking and antioxidants; however, in the context of the metallic components, complications pertaining to corrosion and metal ion release inside the body still persist. Through this review article, we explore the use of DLC coatings on metallic bearing surfaces and elucidate why this technology might be a viable solution for ongoing electrochemical challenges in orthopedics. The different characteristics of DLC coatings and their feasibility in TJRs are examined through assessment of tribo-material characterization methods. A holistic characterization of the coating-substrate interface and the wear performance of such systems are discussed. As with all biomaterials used in TJRs, we need mindful consideration of potential in-vivo challenges. We present a few caveats for DLC coatings including delamination, hydrophobicity, and other conflicting as well as outdating findings in the literature. We recommend prudently exploring DLC films as potential coatings on metallic TJR components to solve the problems pertaining to wear, metal ion release, and corrosion. Ultimately, we advise bringing DLC into clinical use only after addressing all challenges and concerns outlined in this article.

Humanized L184Q Mutated Surfactant Protein C Gene Alters Alveolar Type 2 Epithelial Cell Fate

Alveolar type 2 epithelial (AT2) cells synthesize surfactant protein C (SPC) and repair an injured alveolar epithelium. A mutated surfactant protein C gene (SftpcL184Q, Gene ID: 6440) in newborns has been associated with respiratory distress syndrome and pulmonary fibrosis. However, the underlying mechanisms causing Sftpc gene mutations to regulate AT2 lineage remain unclear. We utilized three-dimensional (3D) feeder-free AT2 organoids in vitro to simulate the alveolar epithelium and compared AT2 lineage characteristics between WT (C57BL/6) and SftpcL184Q mutant mice using colony formation assays, immunofluorescence, flow cytometry, qRT-PCR, and Western blot assays. The AT2 numbers were reduced significantly in SftpcL184Q mice. Organoid numbers and colony-forming efficiency were significantly attenuated in the 3D cultures of primary SftpcL184Q AT2 cells compared to those of WT mice. Podoplanin (PDPN, Alveolar type 1 cell (AT1) marker) expression and transient cell count was significantly increased in SftpcL184Q organoids compared to in the WT mice. The expression levels of CD74, heat shock protein 90 (HSP90), and ribosomal protein S3A1 (RPS3A1) were not significantly different between WT and SftpcL184Q AT2 cells. This study demonstrated that humanized SftpcL184Q mutation regulates AT2 lineage intrinsically. This regulation is independent of CD74, HSP90, and RPS3A1 pathways.

FBLN2 is associated with basal cell markers Krt14 and ITGB1 in mouse mammary epithelial cells and has a preferential expression in molecular subtypes of human breast cancer

BACKGROUND

Fibulin-2 (FBLN2) is a secreted extracellular matrix (ECM) glycoprotein and has been identified in the mouse mammary gland, in cap cells of terminal end buds (TEBs) during puberty, and around myoepithelial cells during early pregnancy. It is required for basement membrane (BM) integrity in mammary epithelium, and its loss has been associated with human breast cancer invasion. Herein, we attempted to confirm the relevance of FBLN2 to myoepithelial phenotype in mammary epithelium and to assess its expression in molecular subtypes of human breast cancer.

METHODS

The relationship between FBLN2 expression and epithelial markers was investigated in pubertal mouse mammary glands and the EpH4 mouse mammary epithelial cell line using immunohistochemistry, immunocytochemistry, and immunoblotting. Human breast cancer mRNA data from the METABRIC and TCGA datasets from Bioportal were analyzed to assess the association of Fbln2 expression with epithelial markers, and with molecular subtypes. Survival curves were generated using data from the METABRIC dataset and the KM databases.

RESULTS

FBLN2 knockdown in mouse mammary epithelial cells was associated with a reduction in KRT14 and an increase in KRT18. Further, TGFβ3 treatment resulted in the upregulation of FBLN2 in vitro. Meta-analyses of human breast cancer datasets from Bioportal showed a higher expression of Fbln2 mRNA in claudin-low, LumA, and normal-like breast cancers compared to LumB, Her2 +, and Basal-like subgroups. Fbln2 mRNA levels were positively associated with mesenchymal markers, myoepithelial markers, and markers of epithelial-mesenchymal transition. Higher expression of Fbln2 mRNA was associated with better prognosis in less advanced breast cancer and this pattern was reversed in more advanced lesions.

CONCLUSION

With further validation, these observations may offer a molecular prognostic tool for human breast cancer for more personalized therapeutic approaches.

Active Surface-Enhanced Raman Spectroscopy (SERS): A Novel Concept for Enhancing Signal Contrast in Complex Matrices Using External Perturbation

Noninvasive detection of surface-enhanced Raman spectroscopy (SERS) signals from deep within tissue represents a common challenge in many biological and clinical applications including disease diagnosis and therapy monitoring. Such signals are typically weak and not readily discernible from often much larger Raman and fluorescence background signals (e.g., from surrounding tissue). Consequently, suboptimal sensitivity in the detection of SERS signals is often achieved in these situations. Similar issues can arise in SERS measurements in other diffusely scattering samples and complex matrices. Here, we propose a novel concept, active SERS, for the efficient retrieval of SERS signals from deep within complex matrices such as biological tissues that mitigates these issues. It relies on applying an external perturbation to the sample to alter the SERS signal from nanoparticles (NPs) deep inside the matrix. A measurement with and without, or before and after, such perturbation then can provide powerful contrasting data enabling an effective elimination of the matrix signals to reveal more clearly the desired SERS signal without the interfering background and associated artifacts. The concept is demonstrated using ultrasound (US) as an external source of perturbation and SERS NPs inserted deep within a heterogeneous tissue phantom mimicking a cluster of NPs accumulated within a small target lesion. The overall SERS signal intensity induced by the applied US perturbation decreased by ∼21% and the SERS signal contrast was considerably improved by eliminating subtraction artifacts present in a conventional measurement performed at a neighboring spatial location in a heterogeneous tissue sample. Although the technique was demonstrated with SERS gold NPs with a standard Raman label, it is envisaged that active SERS NPs (both the nanoscale metal geometry and Raman label) could be specifically designed to deliver an augmented response to the external stimulus to further enhance the achievable SERS signal contrast and yield even greater improvement in detection sensitivity. The method was demonstrated using transmission Raman spectroscopy; however, it is also applicable to other Raman implementations including spatially offset Raman spectroscopy and conventional Raman spectroscopy performed both at depth and at surfaces of complex matrices.

Beyond Newton's law of cooling in evaluating magnetic hyperthermia performance: a device-independent procedure

Accurate knowledge of the heating performance of magnetic nanoparticles (MNPs) under AC magnetic fields is critical for the development of hyperthermia-mediated applications. Usually reported in terms of the specific loss power (SLP) obtained from the temperature variation (ΔT) vs. time (t) curve, such an estimate is subjected to a huge uncertainty. Thus, very different SLP values are reported for the same particles when measured on different equipment/in different laboratories. This lack of control clearly hampers the further development of nanoparticle-mediated heat-triggered technologies. Here, we report a device-independent approach to calculate the SLP value of a suspension of magnetic nanoparticles: the SLP is obtained from the analysis of the peak at the AC magnetic field on/off switch of the ΔT(time) curve. The measurement procedure, which itself constitutes a change of paradigm within the field, is based on the heat diffusion equation, which is still valid when the assumptions of Newton's law of cooling are not applicable, as (i) it corresponds to the ideal scenario in which the temperature profiles of the system during heating and cooling are the same; and (ii) it diminishes the role of coexistence of various heat dissipation channels. Such an approach is supported by theoretical and computational calculations to increase the reliability and reproducibility of SLP determination. Furthermore, the new methodological approach is experimentally confirmed, by magnetic hyperthermia experiments performed using 3 different devices located in 3 different laboratories. Furthermore, the application of this peak analysis method (PAM) to a rapid succession of stimulus on/off switches which results in a zigzag-like ΔT(t), which we term the zigzag protocol, allows evaluation of possible variations of the SLP values with time or temperature.

A python library for the fast and scalable computation of biologically meaningful individual specific networks

Individual Specific Networks (ISNs) are a tool used in computational biology to infer Individual Specific relationships between biological entities from omics data. ISNs provide insights into how the interactions among these entities affect their respective functions. To address the scarcity of solutions for efficiently computing ISNs on large biological datasets, we present ISN-tractor, a data-agnostic, highly optimized Python library to build and analyse ISNs. ISN-tractor demonstrates superior scalability and efficiency in generating Individual Specific Networks (ISNs) when compared to existing methods such as LionessR, both in terms of time and memory usage, allowing ISNs to be used on large datasets. We show how ISN-tractor can be applied to real-life datasets, including The Cancer Genome Atlas (TCGA) and HapMap, showcasing its versatility. ISN-tractor can be used to build ISNs from various -omics data types, including transcriptomics, proteomics, and genotype arrays, and can detect distinct patterns of gene interactions within and across cancer types. We also show how Filtration Curves provided valuable insights into ISN characteristics, revealing topological distinctions among individuals with different clinical outcomes. Additionally, ISN-tractor can effectively cluster populations based on genetic relationships, as demonstrated with Principal Component Analysis on HapMap data.

Targeted Cancer Therapy-on-A-Chip

Targeted cancer therapy (TCT) is gaining increased interest because it reduces the risks of adverse side effects by specifically treating tumor cells. TCT testing has traditionally been performed using two-dimensional (2D) cell culture and animal studies. Organ-on-a-chip (OoC) platforms have been developed to recapitulate cancer in vitro, as cancer-on-a-chip (CoC), and used for chemotherapeutics development and testing. This review explores the use of CoCs to both develop and test TCTs, with a focus on three main aspects, the use of CoCs to identify target biomarkers for TCT development, the use of CoCs to test free, un-encapsulated TCTs, and the use of CoCs to test encapsulated TCTs. Despite current challenges such as system scaling, and testing externally triggered TCTs, TCToC shows a promising future to serve as a supportive, pre-clinical platform to expedite TCT development and bench-to-bedside translation.

From Plastic Waste to Green Hydrogen and Valuable Chemicals Using Sunlight and Water

Over 79 % of 6.3 billion tonnes of plastics produced from 1950 to 2015 have been disposed in landfills or found their way to the oceans, where they will reside for up to hundreds of years before being decomposed bringing upon significant dangers to our health and ecosystems. Plastic photoreforming offers an appealing alternative by using solar energy and water to transform plastic waste into value-added chemical commodities, while simultaneously producing green hydrogen via the hydrogen evolution reaction. This review aims to provide an overview of the underlying principles of emerging plastic photoreforming technologies, highlight the challenges associated with experimental protocols and performance assessments, discuss recent global breakthroughs on the photoreforming of plastics, and propose perspectives for future research. A critical assessment of current plastic photoreforming studies shows a lack of standardised conditions, hindering comparison amongst photocatalyst performance. Guidelines to establish a more accurate evaluation of materials and systems are proposed, with the aim to facilitate the translation of promising fundamental discovery in photocatalysts design.

A competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice

This study explores the impact of densely-ionizing radiation on non-cancer and cancer diseases, focusing on dose, fractionation, age, and sex effects. Using historical mortality data from approximately 21,000 mice exposed to fission neutrons, we employed random survival forest (RSF), a powerful machine learning algorithm accommodating nonlinear dependencies and interactions, treating cancer and non-cancer outcomes as competing risks. Unlike traditional parametric models, RSF avoids strict assumptions and captures complex data relationships through decision tree ensembles. SHAP (SHapley Additive exPlanations) values and variable importance scores were employed for interpretation. The findings revealed clear dose-response trends, with cancer being the predominant cause of mortality. SHAP value dose-response shapes differed, showing saturation for cancer hazard at high doses (> 2 Gy) and a more linear pattern at lower doses. Non-cancer responses remained more linear throughout the entire dose range. There was a potential inverse dose rate effect for cancer, while the evidence for non-cancer was less conclusive. Sex and age effects were less pronounced. This investigation, utilizing machine learning, enhances our understanding of the patterns of non-cancer and cancer mortality induced by densely-ionizing radiations, emphasizing the importance of such approaches in radiation research, including space travel and radioprotection.

Mechanism and application of gas phase trapping by spontaneous imbibition

For fractured gas reservoirs with strong water drive, gas phase trapping affects the gas recovery significantly. The recovery may be less than 50% for some reservoirs while it is only 12% for Beaver River gas field. The gas phase trapping mechanism has been revealed by the results of depletion experimental test. The residual pressure of the trapped gas is as high as 11.75 MPa with a 12.8 cm imbibition layer resulting in gas recovery deceased 49.5% compared with that without imbibition layer. A mathematical model is built to calculate the imbibition thickness based on capillary pressure and relative permeability of the matrix. The gas phase trapping are analyzed by two representative wells in Weiyuan gas field, the intermittent production reinforces the imbibition thickness and result in gas trapped in the matrix block with high residual pressure for the low performace gas wells, the extremely low gas recovery can be explained more rationally. That lays a foundation of improving the gas recovery for fractured reservoirs.

Unveiling novel double-negative prostate cancer subtypes through single-cell RNA sequencing analysis

Recent advancements in single-cell RNA sequencing (scRNAseq) have facilitated the discovery of previously unrecognized subtypes within prostate cancer (PCa), offering new insights into cancer heterogeneity and progression. In this study, we integrated scRNAseq data from multiple studies, comprising publicly available cohorts and data generated by our research team, and established the Human Prostate Single cell Atlas (HuPSA) and Mouse Prostate Single cell Atlas (MoPSA) datasets. Through comprehensive analysis, we identified two novel double-negative PCa populations: KRT7 cells characterized by elevated KRT7 expression and progenitor-like cells marked by SOX2 and FOXA2 expression, distinct from NEPCa, and displaying stem/progenitor features. Furthermore, HuPSA-based deconvolution re-classified human PCa specimens, validating the presence of these novel subtypes. We then developed a user-friendly web application, "HuPSA-MoPSA" ( https://pcatools.shinyapps.io/HuPSA-MoPSA/ ), for visualizing gene expression across all newly established datasets. Our study provides comprehensive tools for PCa research and uncovers novel cancer subtypes that can inform clinical diagnosis and treatment strategies.