Re-epithelialization and immune cell behaviour in an ex vivo human skin model

Secure and privacy-preserving automated machine learning operations into end-to-end integrated IoT-edge-artificial intelligence-blockchain monitoring system for diabetes mellitus prediction

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to be able to monitor and predict the incidence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and ensure security and privacy of the user's data. We provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out within our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system predicts diabetes using RF with 4.57% more accuracy on average in comparison with the other models LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. When using feature selection, the performance is improved by 1.14% for PIMA Indian and 0.02% for Sylhet datasets, while it is reduced by 0.89% for MIMIC III.

Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates

Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus - a highly pathogenic, C. elegans-related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans, we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus. Two of these genes (i.e. Hc-eef-1A and Hc-eef1G), coding for eukaryotic elongation factors (called Hc-eEF1A and Hc-eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc-eEF1A and/or Hc-eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus. Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus, and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates.

Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading

Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity. FACL enhances model generalization by maximizing attention consistency between local clients and the server model. To ensure privacy and validate robustness, we incorporated differential privacy by introducing noise during parameter transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of 0.9718, outperforming seven centers with an average AUC of 0.9499 when categories are relatively balanced. For the Gleason grading task, FACL attained a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI training model for prostate cancer pathology while maintaining effective data safeguards.

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.

Mathematical assessment of the roles of age heterogeneity and vaccination on the dynamics and control of SARS-CoV-2

The COVID-19 pandemic, caused by SARS-CoV-2, disproportionately affected certain segments of society, particularly the elderly population (which suffered the brunt of the burden of the pandemic in terms of severity of the disease, hospitalization, and death). This study presents a generalized multigroup model, with m heterogeneous sub-populations, to assess the population-level impact of age heterogeneity and vaccination on the transmission dynamics and control of the SARS-CoV-2 pandemic in the United States. Rigorous analysis of the model for the homogeneous case (i.e., the model with m = 1) reveal that its disease-free equilibrium is globally-asymptotically stable for two special cases (with perfect vaccine efficacy or negligible disease-induced mortality) whenever the associated reproduction number is less than one. The model has a unique and globally-asymptotically stable endemic equilibrium, for special a case, when the associated reproduction threshold exceeds one. The homogeneous model was fitted using the observed cumulative mortality data for the United States during three distinct waves (Waves A (October 17, 2020 to April 5, 2021), B (July 9, 2021 to November 7, 2021) and C (January 1, 2022 to May 7, 2022)) chosen to align with time periods when the Alpha, Delta and Omicron were, respectively, the predominant variants in the United States. The calibrated model was used to derive a theoretical expression for achieving vaccine-derived herd immunity (needed to eliminate the disease in the United States). It was shown that, using the one-group homogeneous model, vaccine-derived herd immunity is not attainable during Wave C of the pandemic in the United States, regardless of the coverage level of the fully-vaccinated individuals. Global sensitivity analysis was carried out to determine the parameters of the model that have the most influence on the disease dynamics and burden. These analyses reveal that control and mitigation strategies that may be very effective during one wave may not be so very effective during the other wave or waves. However, strategies that target asymptomatic and pre-symptomatic infectious individuals are shown to be consistently effective across all waves. To study the impact of the disproportionate effect of COVID-19 on the elderly population, we considered the heterogeneous model for the case where the total population is subdivided into the sub-populations of individuals under 65 years of age and those that are 65 and older. The resulting two-group heterogeneous model, which was also fitted using the cumulative mortality data for wave C, was also rigorously analysed. Unlike for the case of the one-group model, it was shown, for the two-group model, that vaccine-derived herd immunity can indeed be achieved during Wave C of the pandemic if at least 61% of the populace is fully vaccinated. Thus, this study shows that adding age heterogeneity into a SARS-CoV-2 vaccination model with homogeneous mixing significantly reduces the level of vaccination coverage needed to achieve vaccine-derived herd immunity (specifically, for the heterogeneous model, herd-immunity can be attained during Wave C if a moderate proportion of susceptible individuals are fully vaccinated). The consequence of this result is that vaccination models for SARS-CoV-2 that do not explicitly account for age heterogeneity may be overestimating the level of vaccine-derived herd immunity threshold needed to eliminate the SARS-CoV-2 pandemic.

Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy

Purpose

Automated machine learning (AutoML) has emerged as a novel tool for medical professionals lacking coding experience, enabling them to develop predictive models for treatment outcomes. This study evaluated the performance of AutoML tools in developing models predicting the success of pneumatic retinopexy (PR) in treatment of rhegmatogenous retinal detachment (RRD). These models were then compared with custom models created by machine learning (ML) experts.

Design

Retrospective multicenter study.

Participants

Five hundred and thirty nine consecutive patients with primary RRD that underwent PR by a vitreoretinal fellow at 6 training hospitals between 2002 and 2022.

Methods

We used 2 AutoML platforms: MATLAB Classification Learner and Google Cloud AutoML. Additional models were developed by computer scientists. We included patient demographics and baseline characteristics, including lens and macula status, RRD size, number and location of breaks, presence of vitreous hemorrhage and lattice degeneration, and physicians' experience. The dataset was split into a training (n = 483) and test set (n = 56). The training set, with a 2:1 success-to-failure ratio, was used to train the MATLAB models. Because Google Cloud AutoML requires a minimum of 1000 samples, the training set was tripled to create a new set with 1449 datapoints. Additionally, balanced datasets with a 1:1 success-to-failure ratio were created using Python.

Main Outcome Measures

Single-procedure anatomic success rate, as predicted by the ML models. F2 scores and area under the receiver operating curve (AUROC) were used as primary metrics to compare models.

Results

The best performing AutoML model (F2 score: 0.85; AUROC: 0.90; MATLAB), showed comparable performance to the custom model (0.92, 0.86) when trained on the balanced datasets. However, training the AutoML model with imbalanced data yielded misleadingly high AUROC (0.81) despite low F2-score (0.2) and sensitivity (0.17).

Conclusions

We demonstrated the feasibility of using AutoML as an accessible tool for medical professionals to develop models from clinical data. Such models can ultimately aid in the clinical decision-making, contributing to better patient outcomes. However, outcomes can be misleading or unreliable if used naively. Limitations exist, particularly if datasets contain missing variables or are highly imbalanced. Proper model selection and data preprocessing can improve the reliability of AutoML tools.

Financial Disclosures

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

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.

Exploring the impact of circRNAs on cancer glycolysis: Insights into tumor progression and therapeutic strategies

Cancer cells exhibit altered metabolic pathways, prominently featuring enhanced glycolytic activity to sustain their rapid growth and proliferation. Dysregulation of glycolysis is a well-established hallmark of cancer and contributes to tumor progression and resistance to therapy. Increased glycolysis supplies the energy necessary for increased proliferation and creates an acidic milieu, which in turn encourages tumor cells' infiltration, metastasis, and chemoresistance. Circular RNAs (circRNAs) have emerged as pivotal players in diverse biological processes, including cancer development and metabolic reprogramming. The interplay between circRNAs and glycolysis is explored, illuminating how circRNAs regulate key glycolysis-associated genes and enzymes, thereby influencing tumor metabolic profiles. In this overview, we highlight the mechanisms by which circRNAs regulate glycolytic enzymes and modulate glycolysis. In addition, we discuss the clinical implications of dysregulated circRNAs in cancer glycolysis, including their potential use as diagnostic and prognostic biomarkers. All in all, in this overview, we provide the most recent findings on how circRNAs operate at the molecular level to control glycolysis in various types of cancer, including hepatocellular carcinoma (HCC), prostate cancer (PCa), colorectal cancer (CRC), cervical cancer (CC), glioma, non-small cell lung cancer (NSCLC), breast cancer, and gastric cancer (GC). In conclusion, this review provides a comprehensive overview of the significance of circRNAs in cancer glycolysis, shedding light on their intricate roles in tumor development and presenting innovative therapeutic avenues.

Pilot market surveillance of GMM contaminations in alpha-amylase food enzyme products: A detection strategy strengthened by a newly developed qPCR method targeting a GM Bacillus licheniformis producing alpha-amylase

Using high-throughput metagenomics on commercial microbial fermentation products, DNA from a new unauthorized genetically modified microorganism (GMM), namely the GM B. licheniformis strain producing alpha-amylase (GMM alpha-amylase2), was recently discovered and characterized. On this basis, a new qPCR method targeting an unnatural association of sequences specific to the GMM alpha-amylase2 strain was designed and developed in this study, allowing to strengthen the current GMM detection strategy. The performance of the newly developed qPCR method was assessed for its specificity and sensitivity to comply with the minimum performance requirements established by the European Network of GMO Laboratories for GMO analysis. Moreover, the transferability of the in house validated qPCR method was demonstrated. Finally, its applicability was confirmed by a pilot market surveillance of GMM contaminations conducted for the first time on 40 alpha-amylase food enzyme products labelled as containing alpha-amylase. This pilot market surveillance allowed also to highlight numerous contaminations with GMM alpha-amylase2, including frequent cross-contaminations with other GMM strains previously characterized. In addition, the presence of full-length AMR genes, raising health concerns, was also reported.

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.

Elucidating the role of monoacetylphlorogulcinol in the pathogenicity of Pseudomonas 'gingeri' against Agaricus bisporus

BACKGROUND

Agaricus bisporus is a globally important edible fungus. The occurrence of ginger blotch caused by Pseudomonas 'gingeri' during A. bisporus growth and post-harvest stages results in significant economic losses. The biotoxin monoacetylphloroglucinol (MAPG) produced by P. 'gingeri' is responsible for inducing ginger blotch on A. bisporus. However, the understanding of the toxic mechanisms of MAPG on A. bisporus remains limited, which hinders the precise control of ginger blotch disease in A. bisporus and the breeding of disease-resistant varieties.

RESULTS

Integrating transcriptomic, metabolomic, and physiological data revealed that MAPG led to an increase in intracellular superoxide anion (O2 -) levels and lipid peroxidation in A. bisporus. MAPG changed the cellular membrane composition of A. bisporus, causing to damage membrane permeability. MAPG inhibited the expression of genes associated with the 19s subunit of the proteasome, thereby impeding cellular waste degradation in A. bisporus. Unlike melanin, MAPG stimulated the synthesis of flavonoids in A. bisporus, which might explain the manifestation of ginger-colored symptoms rather than browning. Meanwhile, the glutathione metabolism pathway in A. bisporus played a pivotal role in counteracting the cytotoxic effects of MAPG. Additionally, enhanced catalase activity and up-regulation of defense-related genes, including cytochrome P450s, Major Facilitator Superfamily (MFS), and ABC transporters, were observed.

CONCLUSION

This study provides comprehensive insights into MAPG toxicity in A. bisporus and uncovers the detoxification strategies of A. bisporus against MAPG. The findings offer valuable evidence for precise control and breeding of resistant varieties against ginger blotch in A. bisporus. © 2024 Society of Chemical Industry.

A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network

Epilepsy is one of the most common brain disorders, and seizures of epilepsy have severe adverse effects on patients. Real-time epilepsy seizure detection using electroencephalography (EEG) signals is an important research area aimed at improving the diagnosis and treatment of epilepsy. This paper proposed a real-time approach based on EEG signal for detecting epilepsy seizures using the STFT and Google-net convolutional neural network (CNN). The CHB-MIT database was used to evaluate the performance, and received the results of 97.74 % in accuracy, 98.90 % in sensitivity, 1.94 % in false positive rate. Additionally, the proposed method was implemented in a real-time manner using the sliding window technique. The processing time of the proposed method just 0.02 s for every 2-s EEG episode and achieved average 9.85- second delay in each seizure onset.

Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants

Nonsense and missense mutations in the transcription factor PAX6 cause a wide range of eye development defects, including aniridia, microphthalmia and coloboma. To understand how changes of PAX6:DNA binding cause these phenotypes, we combined saturation mutagenesis of the paired domain of PAX6 with a yeast one-hybrid (Y1H) assay in which expression of a PAX6-GAL4 fusion gene drives antibiotic resistance. We quantified binding of more than 2700 single amino-acid variants to two DNA sequence elements. Mutations in DNA-facing residues of the N-terminal subdomain and linker region were most detrimental, as were mutations to prolines and to negatively charged residues. Many variants caused sequence-specific molecular gain-of-function effects, including variants in position 71 that increased binding to the LE9 enhancer but decreased binding to a SELEX-derived binding site. In the absence of antibiotic selection, variants that retained DNA binding slowed yeast growth, likely because such variants perturbed the yeast transcriptome. Benchmarking against known patient variants and applying ACMG/AMP guidelines to variant classification, we obtained supporting-to-moderate evidence that 977 variants are likely pathogenic and 1306 are likely benign. Our analysis shows that most pathogenic mutations in the paired domain of PAX6 can be explained simply by the effects of these mutations on PAX6:DNA association, and establishes Y1H as a generalisable assay for the interpretation of variant effects in transcription factors.

Clinical indications and protocol considerations for selecting initial body weight support levels in gait rehabilitation: a systematic review

BACKGROUND

Body weight support (BWS) training devices are frequently used to improve gait in individuals with neurological impairments, but guidance in selecting an appropriate level of BWS is limited. Here, we aim to describe the initial BWS levels used during gait training, the rationale for this selection and the clinical goals aligned with BWS training for different diagnoses.

METHOD

A systematic literature search was conducted in PubMed, Embase and Web of Science, including terms related to the population (individuals with neurological disorders), intervention (BWS training) and outcome (gait). Information on patient characteristics, type of BWS device, BWS level and training goals was extracted from the included articles.

RESULTS

Thirty-three articles were included, which described outcomes using frame-based (stationary or mobile) and unidirectional ceiling-mounted devices on four diagnoses (multiple sclerosis (MS), spinal cord injury (SCI), stroke, traumatic brain injury (TBI)). The BWS levels were highest for individuals with MS (median: 75%, IQR: 6%), followed by SCI (median: 40%, IQR: 35%), stroke (median: 30%, IQR: 4.75%) and TBI (median: 15%, IQR: 0%). The included studies reported eleven different training goals. Reported BWS levels ranged between 30 and 75% for most of the training goals, without a clear relationship between BWS level, diagnosis, training goal and rationale for BWS selection. Training goals were achieved in all included studies.

CONCLUSION

Initial BWS levels differ considerably between studies included in this review. The underlying rationale for these differences was not clearly motivated in the included studies. Variation in study designs and populations does not allow to draw a conclusion on the effectiveness of BWS levels. Hence, it remains difficult to formulate guidelines on optimal BWS settings for different diagnoses, BWS devices and training goals. Further efforts are required to establish clinical guidelines and to experimentally investigate which initial BWS levels are optimal for specific diagnoses and training goals.

Auditory gamma-band entrainment enhances default mode network connectivity in dementia patients

Dementia, and in particular Alzheimer's disease (AD), can be characterized by disrupted functional connectivity in the brain caused by beta-amyloid deposition in neural links. Non-pharmaceutical treatments for dementia have recently explored interventions involving the stimulation of neuronal populations in the gamma band. These interventions aim to restore brain network functionality by synchronizing rhythmic energy through various stimulation modalities. Entrainment, a newly proposed non-invasive sensory stimulation method, has shown promise in improving cognitive functions in dementia patients. This study investigates the effectiveness of entrainment in terms of promoting neural synchrony and spatial connectivity across the cortex. EEG signals were recorded during a 40 Hz auditory entrainment session conducted with a group of elderly participants with dementia. Phase locking value (PLV) between different intraregional and interregional sites was examined as an attribute of network synchronization, and connectivity of local and distant links were compared during the stimulation and rest trials. Our findings demonstrate enhanced neural synchrony between the frontal and parietal regions, which are key components of the brain's default mode network (DMN). The DMN operation is known to be impacted by dementia's progression, leading to reduced functional connectivity across the parieto-frontal pathways. Notably, entrainment alone significantly improves synchrony between these DMN components, suggesting its potential for restoring functional connectivity.

Biosorption of copper, nickel, and manganese as well as the production of metal nanoparticles by Bacillus species isolated from soils contaminated with electronic wastes

Improper electronic waste management in the world especially in developing countries such as Iran has resulted in environmental pollution. Copper, nickel, and manganese are from the most concerned soil contaminating heavy metals which found in many electronic devices that are not properly processed. The aim of this study was to investigate the biological removal of copper, nickel, and manganese by Bacillus species isolated from a landfill of electronic waste (Zainal Pass hills located in Isfahan, Iran) which is the and to produce nanoparticles from the studied metals by the isolated bacteria. The amounts of copper, nickel, and manganese in the soil was measured as 1.9 × 104 mg/kg, 0.011 × 104 mg/kg and 0.013 × 104 mg/kg, respectively based on ICP-OES analysis, which was significantly higher than normal (0.02 mg/kg, 0.05 mg/kg, and 2 mg/kg, respectively. The minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC) of metals on the bacterial isolates was determined. The biosorption of metals by the bacteria was evaluated by inductively coupled plasma optical emission spectroscopy (ICP-OES). The metal nanoparticles were synthetized utilizing the isolates in culture media containing the heavy metals with the concentrations to which the isolates had shown resistance. X ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) were used for the evaluation of the fabrication of the produced metal nanoparticles. Based on the findings of this study, a total of 15 bacterial isolates were obtained from the soil samples. The obtained MICs of copper, nickel, and manganese on the isolates were 40-300 mM, 4-10 mM, and 60-120 mM, respectively. The most resistant isolates to copper were FM1 and FM2 which were able to bio-remove 79.81% and 68.69% of the metal, respectively. FM4 and FM5 were respectively the most resistant isolate to nickel and manganese and were able to bio-remove 86.74% and 91.96% of the metals, respectively. FM1, FM2, FM4, and FM5 was molecularly identified as Bacillus cereus, Bacillus thuringiensis, Bacillus paramycoides, and Bacillus wiedmannii, respectively. The results of XRD, SEM and EDS showed conversion of the copper and manganese into spherical and oval nanoparticles with the approximate sizes of 20-40 nm. Due to the fact that the novel strains in this study showed high resistance to copper, nickel, and manganese and high adsorption of the metals, they can be used in the future, as suitable strains for the bio-removal of these metals from electronic and other industrial wastes.

Estimating the health impacts of exposure to Air pollutants and the evaluation of changes in their concentration using a linear model in Iran

In big and industrial cities of developing countries, illness and mortality from long-term exposure to air pollutants have become a serious issue. This research was carried out in 2019-2020 to estimate the health impacts of PM10, NO2 and O3 pollutants by using AirQ+ and R statistical programming software in Arak, Isfahan, Tabriz, Shiraz, Karaj, and Mashhad. Mortality statistics, number of people in required age groups, and amount of pollutants were gathered respectively from different agencies like Statistics and Information Technology of the Ministry of Health, Statistical Center, and Department of Environment and by using Excel, the average 24-hour and 1-hour concentration and maximum 8-hour concentration for PM10, NO2 and O3 pollutants were gathered. We used linear mixed impacts model to account for the longitudinal observations and heterogeneity of the cities. The results of the study showed high number of deaths due to chronic bronchitis in adults, premature death of infants, and respiratory diseases in Mashhad. This research highlights the importance of estimation of health impacts from exposure to air pollutants on residents of the studied cities.

Changes in Sperm Parameters with Time in Men with Normal and Abnormal Baseline Semen Analysis

The association between paternal age and sperm quality in the population level has been previously studied. Only limited data exists regarding the intra-personal variations in semen parameters among fertile and infertile men over time. We aimed to assess trends over time in semen parameters among men with normal and abnormal baseline sperm parameters and investigate potential risk factors for sperm quality deterioration. This retrospective cohort study was conducted at a university-affiliated medical center in vitro fertilization (IVF) unit. Patients with at least two semen analyses (SA) performed > 1 year apart, with the last SA done between 2017 and 2021, were included. The study consisted of two main analyses-comparison of intra-patient's sperm parameters changes in men with normal and abnormal baseline SA (BSA) and analysis of risk factors for developing abnormal semen parameters over time in men who had normal BSA parameters. This study included a total of 902 men assessed for infertility with normal and abnormal BSA. The average time interval between tests was 1015 days (range 366-7709 days). Among individuals with normal BSA, there was a mild decline in most parameters-concentration (- 6.53 M/ml), motility (- 7.74%), and total motile count (TMC) (- 21.80 M) (p < 0.05 for all parameters). In contrast, a slight improvement in most parameters, except for concentration, was noted in men with abnormal BSA-volume (+ 0.21 ml), motility (+ 8.72%), and TMC (+ 14.38 M) (p < 0.05 for all parameters). Focusing on men with normal BSA, 33.5% of individuals developed abnormality in one or more of their sperm parameters over time, within a mean time of 1013 ± 661 days. We also found that only time between tests emerged as an independent prognostic factor for the development of abnormal SA later. Interestingly, sperm deterioration in participants in their third, fourth, and fifth decades of life with normal initial semen analysis was similar. Our study provides evidence of a decline in semen quality over time in individuals with normal BSA, in contrast to men with abnormal BSA. Longer time intervals between tests independently increase the risk of sperm abnormalities.

Multiple-criteria analysis of the employee satisfaction level at healthcare facilities during the pandemic

Due to the pandemic, the entire staff employed in healthcare institutions were directly exposed to longer working hours and unstable working conditions. Based on these facts, the research in this article aims to find out more about employees' satisfaction with working conditions and management's engagement in preserving their health and safety. Thus, a multiple-criteria decision analysis (MCDA) model based on the analyzed attitudes and opinions of employees was formed to reveal the interdependencies between working conditions resulting from the undertaken management activities, employee satisfaction and workplace safety. The results show that both nurses and drivers were least satisfied with working conditions during the pandemic while indicating those workplaces to be the most endangered. Additionally, the proposed model can be successfully applied to any type of organization to identify insufficiently effective management activities, address them accordingly and thereby improve the level of employee satisfaction.

Human liver organoids: From generation to applications

In the last decade, research into human hepatology has been revolutionized by the development of mini human livers in a dish. These liver organoids are formed by self-organizing stem cells and resemble their native counterparts in cellular content, multicellular architecture, and functional features. Liver organoids can be derived from the liver tissue or pluripotent stem cells generated from a skin biopsy, blood cells, or renal epithelial cells present in urine. With the development of liver organoids, a large part of previous hurdles in modeling the human liver is likely to be solved, enabling possibilities to better model liver disease, improve (personalized) drug testing, and advance bioengineering options. In this review, we address strategies to generate and use organoids in human liver disease modeling, followed by a discussion of their potential application in drug development and therapeutics, as well as their strengths and limitations.

Application of experimental design as a statistical approach to recover bioactive peptides from different food sources

Bioactive peptides (BAPs) derived from samples of animals and plants have been widely recommended and consumed for their beneficial properties to human health and to control several diseases. This work presents the applications of experimental designs (DoE) used to perform factor screening and/or optimization focused on finding the ideal hydrolysis condition to obtain BAPs with specific biological activities. The collection and discussion of articles revealed that Box Behnken Desing and Central Composite Design were the most used. The main parameters evaluated were pH, time, temperature and enzyme/substrate ratio. Among vegetable protein sources, soy was the most used in the generation of BAPs, and among animal proteins, milk and shrimp stood out as the most explored sources. The degree of hydrolysis and antioxidant activity were the most investigated responses in obtaining BAPs. This review brings new information that helps researchers apply these DoE to obtain high-quality BAPs with the desired biological activities.

Uro-pathogens: Multidrug resistance and associated factors of community-acquired UTI among HIV patients attending antiretroviral therapy in Dessie Comprehensive Specialized Hospital, Northeast Ethiopia

BACKGROUND

Urinary tract infections are common bacterial and fungal infections in humans, occurring both in the community and in immunocompromised patients in healthcare settings. Urinary tract infections have a significant health impact on HIV-infected patients. Nowadays, drug-resistant pathogens are widespread poses a serious clinical risk, and causes urinary tract infection. The common agents of bacteria and fungi that cause urinary tract infection are Escherichia coli followed by Klebsiella pneumonia, Staphylococcus saprophyticus, Enterococcus faecalis, group B streptococcus, Proteus mirabilis, Pseudomonas aeruginosa, Staphylococcus aureus and Candida. albicans. This study aimed to investigate uro-pathogen, multidrug resistance pattern of bacteria, and associated factors of community-acquired urinary tract infection among HIV-positive patients attending antiretroviral therapy in Dessie comprehensive specialized hospital, Northeast Ethiopia from February 1, 2021, to March 30, 2021.

METHODS

An institutional-based cross-sectional study was conducted at Dessie Comprehensive Specialized Hospital. Socio-demographic and clinical data were collected by using structured questionnaires from HIV patients suspected of community-acquired urinary tract infections. About 10 ml of clean-catch midstream urine was collected and inoculated into Blood agar, MacConkey, and Cysteine lactose electrolyte deficient media. Yeasts were identified by using Gram stain, germ tube test, carbohydrate fermentation, assimilation tests, and chromogenic medium. Gram stain and biochemical tests were performed to identify isolates and an antimicrobial susceptibility pattern was performed on disc diffusion techniques. Data were entered and analyzed using SPSS version 25. Both bivariate and multivariable logistic regression analysis was performed and a P value of < 0.05 with an adjusted odds ratio with their 95% confidence interval (CI) was used as statistically significant associations.

RESULTS

From the total 346 study participants, 92 (26.6%) were culture positive 75 (81.52%) were bacterial and 17 (18.48%) were fungal pathogens. From a total of 75 bacteria isolates 51(68%) were Gram-negative bacteria and the most commonly isolated bacteria were E. coli 16 (21.33%) followed by K. pneumoniae 11(14.67%) and enterococcus species 10(10.87. Of the 17 fungal isolates of fungi, 8(47.1%) were represented by C. tropicalis. Of the isolated bacteria, 61(81.3%) were resistant to three and above classes of antibiotics (drug classes). About 13 (81.3%) of E. coli, 9(81.8%) of K. pneumoniae, 8(80%) of Enterococcus species, 7 (77.8%) of P. aeruginosa, and CoNs 7(87.5%) were the most frequently exhibited three and above classes of antibiotics (multi-drug resistance). Amikacin and gentamicin were effective against Gram-negative Uro-pathogens. Participants aged>44year, female, being daily labor, being farmer, unable to read and write, patients with CD4 count of ≤ 200 cells/mm3 and CD4 count of 201-350 cells/mm3, who had chronic diabetics, patients having a history of hospitalization and who had urgency of urinations were statistically significant association with significant urinary tract infections.

CONCLUSION

The burden of community-acquired urinary tract infections among HIV patients is alarmingly increased. Therefore, behavior change communications might be considered for promoting the health status of HIV patients. Moreover, CD4 level monitoring and therapeutics selection based on microbiological culture are quite advisable for the management of urinary tract infections of HIV patients.

Identification of Major Threats of Climate Change, Hazards, and Anthropogenic Activities on Biodiversity Conservation in the Buxa Tiger Reserve, India

The Himalayan forests are facing a range of threats, which are making conservation efforts challenging. Using a mixed-method approach, this study investigated the threats to biodiversity conservation in Buxa Tiger Reserve (BTR), a fragile ecosystem in the Eastern Himalayan foothills. The study found that between 1990 and 2021, BTR experienced rising summer temperatures and decreasing annual precipitation, contributing to forest dryness, water scarcity, and forest fires. Natural disasters such as floods, flash floods, earthquakes, and landslides also caused significant damage to wildlife habitats. Changes in land use and land cover, including encroachment, infrastructure development, fuelwood collection, and grazing practices, were also identified as significant drivers of ecosystem alteration. Besides, hunting and poaching also emerged as threats to wildlife populations in the reserve. By employing the Analytic Hierarchy Process (AHP), the study determined that land use change, infrastructure development, climate change, livestock grazing, and fuelwood collection pose significant threats to flora conservation outcomes in BTR, while infrastructure development, climate change, livestock grazing, and forest fires are the primary threats to wildlife conservation outcomes in the reserve. The study recommends the regulation of land use practices, promotion of sustainable livelihoods for local communities, effective conservation strategies, and public awareness and education programs to promote the value of biodiversity conservation.

Lauric acid content in intramuscular fat is a reliable indicator of black soldier fly larvae meal consumption in Muscovy ducks

The present research investigated if intramuscular fatty acid (FA) profile could distinguish meat from ducks fed with black soldier fly larvae meal (BSFLM) during fattening. By using stepwise linear discriminant analysis on FA profiles of 96 meat samples, lauric acid (C12:0) was found to be the best predictor, accurately differentiating samples with only two misclassifications. The Fisher classification functions indicated that breast samples with lauric acid content above 0.222 % in intramuscular fat would be classified as BSFLM-fed. The Fisher classification functions were also effective in other poultry species fed BSFLM, with only two misclassified samples out of 42 samples from a dataset compiled from published papers. Misclassifications were linked to an unexpected lauric acid content in the intramuscular fat. In conclusion, given that BSFLM is the only relevant source of lauric acid in the diet, this fatty acid could identify meat from BSFLM-fed ducks and possibly other poultry species, warranting further exploration of other FA as potential identifiers of BSFLM-fed poultry.

Assessment of biometrics and stress indicators of the adult Village Weaver birds (Ploceus cucullatus) during breeding and post-breeding seasons in Jos, Nigeria

In this era of climate change, some biological conservationists' concerns are based on seasonal studies that highlight how wild birds' physiological fitness are interconnected with the immediate environment to avoid population decline. We investigated how seasonal biometrics correlated to stress parameters of the adult Village Weavers (Ploceus cucullatus) during breeding and post-breeding seasons of the Weaver birds in Amurum Forest Reserve. Specifically, we explored the following objectives: (i) the seasonal number of birds captured; (ii) whether seasonal baseline corticosterone (CORT), packed cell volume (PCV), and heterophil to lymphocytes ratio (H:L) were sex-dependent; (iii) whether H:L ratio varied with baseline (CORT); (iv) whether phenotypic condition (post-breeding moult) and brood patch varied with baseline (CORT) and H:L ratio; and (v) how body biometrics co-varied birds' seasonal baseline (CORT), (PCV) and (H:L) ratio. Trapping of birds (May-November) coincided with breeding and post-breeding seasons. The birds (n = 53 males, 39 females) were ringed, morphologically assessed (body mass, wing length, moult, brood patch) and blood collected from their brachial vein was used to assess CORT, PCV and H:L ratio. Although our results indicated more male birds trapped during breeding, the multiple analyses of variance (MANOVA) indicated that the seasonal temperature of the trapping sites correlated (P < 0.05) significantly to baseline (CORT). The general linear mixed model analyses (GLMMs) indicated that the baseline (CORT) also correlated significantly to H:L ratio of the male and female birds. However, PCV correlated significantly to body size of the birds (wing length) and not body mass. Haematological parameters such as the baseline CORT and the H:L ratio as indicators of stress in wild birds. Hence, there is the possibility that the Village Weaver birds suffered from seasonally induced stress under the constrained effect of environmental temperature. Hence, future studies should investigate whether the effect observed is also attributable to other passerine species.

COVID-19 transmission between the community and meat processing plants in Ireland: A retrospective modelling study

Outbreaks of COVID-19 in meat processing plants (MPPs) were recorded globally throughout the pandemic. There was speculation these outbreaks resulted in dissemination of COVID-19 throughout the surrounding county leading to high incidence rates. We aimed to investigate the dynamics of spread between MPPs and their surrounding counties. In this retrospective longitudinal study, data were collected on the number and size of outbreaks in 33 MPPs and county infections in Ireland between March 2020 and May 2021. These data were used to investigate the relationship between outbreaks in MPPs and county infection rates through statistical analysis, and the development of a novel SEIR model. We found an association between the number of MPPs present in a county and county incidence rates, however, incidence rates in the counties did not increase as a consequence of an outbreak in an MPP. The model results indicate that county incidence rates in the weeks prior to an MPP outbreak could reliably predict the size of that outbreak in a plant, r(49) = 0·62, p < 0·0001, RMSD = 5·6. In Ireland, outbreaks in MPPs were strongly correlated with high levels of infection in the surrounding county, rather than being a driver of infection in the county. The modified SEIR model described here can provide an explanation of the generative process required to cause outbreaks of the size and scale that occur in MPPs.

Development of a multi-step screening procedure for redox active molecules in organic radical polymer anodes and as redox flow anolytes

Benzo[d]-X-zolyl-pyridinyl (XO, S, NH) radicals represent a promising class of redox-active molecules for organic batteries. We present a multistep screening procedure to identify the most promising radical candidates. Experimental investigations and highly correlated wave function-based calculations are performed to determine benchmark redox potentials. Based on these, the accuracies of different methods (semi-empirical, density functional theory, wave function-based), solvent models, dispersion corrections, and basis sets are evaluated. The developed screening procedure consists of three steps: First, a conformer search is performed with CREST. The molecules are selected based on the redox potentials calculated using GFN2-xTB. Second, HOMO energies calculated with reparametrized B3LYP-D3(BJ) and the def2-SVP basis set are used as selection criteria. The final molecules are selected based on the redox potentials calculated from Gibbs energies using BP86-D3(BJ)/def2-TZVP. With this multistep screening approach, promising molecules can be suggested for synthesis, and structure-property relationships can be derived.

Deep learning system for screening AIDS-related cytomegalovirus retinitis with ultra-wide-field fundus images

Background

Ophthalmological screening for cytomegalovirus retinitis (CMVR) for HIV/AIDS patients is important to prevent lifelong blindness. Previous studies have shown good properties of automated CMVR screening using digital fundus images. However, the application of a deep learning (DL) system to CMVR with ultra-wide-field (UWF) fundus images has not been studied, and the feasibility and efficiency of this method are uncertain.

Methods

In this study, we developed, internally validated, externally validated, and prospectively validated a DL system to detect AIDS-related from UWF fundus images from different clinical datasets. We independently used the InceptionResnetV2 network to develop and internally validate a DL system for identifying active CMVR, inactive CMVR, and non-CMVR in 6960 UWF fundus images from 862 AIDS patients and validated the system in a prospective and an external validation data set using the area under the curve (AUC), accuracy, sensitivity, and specificity. A heat map identified the most important area (lesions) used by the DL system for differentiating CMVR.

Results

The DL system showed AUCs of 0.945 (95 % confidence interval [CI]: 0.929, 0.962), 0.964 (95 % CI: 0.870, 0.999) and 0.968 (95 % CI: 0.860, 1.000) for detecting active CMVR from non-CMVR and 0.923 (95 % CI: 0.908, 0.938), 0.902 (0.857, 0.948) and 0.884 (0.851, 0.917) for detecting active CMVR from non-CMVR in the internal cross-validation, external validation, and prospective validation, respectively. Deep learning performed promisingly in screening CMVR. It also showed the ability to differentiate active CMVR from non-CMVR and inactive CMVR as well as to identify active CMVR and inactive CMVR from non-CMVR (all AUCs in the three independent data sets >0.900). The heat maps successfully highlighted lesion locations.

Conclusions

Our UWF fundus image-based DL system showed reliable performance for screening AIDS-related CMVR showing its potential for screening CMVR in HIV/AIDS patients, especially in the absence of ophthalmic resources.

Optimizing ultrasound-assisted extraction of bioactive compounds from Canthium horridum blume leaves utilizing polyols: A study on skin-related activities

The focus on reducing organic solvent usage, owing to their negative environmental and health impacts, is driving a search for innovative green alternative solvents in academia and industry. Canthium horridum Blume (CH) exhibits many therapeutic activities, including antioxidant and anti-inflammatory efficacy. The objective of this study is to evaluate the optimal solvent concentration using a simplex-lattice design with an aqueous-polyols mixture and to optimize the parameters for extracting bioactive compounds and antioxidant activities from ultrasound-assisted extraction (UAE) of CH leaves through central composite design (CCD) in response surface methodology (RSM). This study examines the total phenolic content (TPC), antioxidant activities, comparison of different extraction conditions, identification of bioactive compounds, cell cytotoxicity, cellular antioxidant activity, and melanin content reduction efficacy of the extracts. According to the findings from the simplex-lattice model, the ideal solvent composition consisted of 32.57%w/w butylene glycol, 32.92%w/w glycerine, and 34.51%w/w water. Furthermore, based on the response model, optimal extraction conditions were identified as a 15-min extraction time and a solvent-to-sample ratio of 32.94:1. In comparison to alternative extraction methods, ultrasonic-assisted extraction using the aqueous-glycerine-butylene glycol (GB-UAE) extract resulted in notably elevated TPC and antioxidant responses (p < 0.05). Major antioxidant bioactive compounds included 4-(Butoxymethyl) phenol, 3-O-Caffeoyl-4-O-methylquinic acid, Quercetin 3-(2G-glucosylrutinoside), 2,4-Dihydroxybenzoic acid and other bioactive compounds. The GB-UAE extract revealed greater cell viability than UAE using ethanol (EtOH-UAE) extract in both cytotoxicity and cellular antioxidant assays at the same concentration. Additionally, it exhibited comparable melanin content reduction efficacy at a higher concentration compared to that of EtOH-UAE extract. The researcher anticipates that the current study will advance the utilization of an aqueous-polyols system for extracting bioactive compounds extending beyond CH leaves. Although the potential applications of CH leaves in cosmetics and pharmaceutical formulations have been identified, further comprehensive mechanistic and clinical studies are required to fully understand their effects.

A review of machine learning methods for cancer characterization from microbiome data

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

Bonding states of gold/silver plasmonic nanostructures and sulfur-containing active biological ingredients in biomedical applications: a review

As one of the most instrumental components in the architecture of advanced nanomedicines, plasmonic nanostructures (mainly gold and silver nanomaterials) have been paid a lot of attention. This type of nanomaterial can absorb light photons with a specific wavelength and generate heat or excited electrons through surface resonance, which is a unique physical property. In innovative biomaterials, a significant number of theranostic (therapeutic and diagnostic) materials are produced through the conjugation of thiol-containing ingredients with gold and silver nanoparticles (Au and Ag NPs). Hence, it is essential to investigate Au/Ag-S interfaces precisely and determine the exact bonding states in the active nanobiomaterials. This study intends to provide useful insights into the interactions between Au/Ag NPs and thiol groups that exist in the structure of biomaterials. In this regard, the modeling of Au/Ag-S bonding in active biological ingredients is precisely reviewed. Then, the physiological stability of Au/Ag-based plasmonic nanobioconjugates in real physiological environments (pharmacokinetics) is discussed. Recent experimental validation and achievements of plasmonic theranostics and radiolabelled nanomaterials based on Au/Ag-S conjugation are also profoundly reviewed. This study will also help researchers working on biosensors in which plasmonic devices deal with the thiol-containing biomaterials (e.g., antibodies) inside blood serum and living cells.

A multistage framework for respiratory disease detection and assessing severity in chest X-ray images

Chest Radiography is a non-invasive imaging modality for diagnosing and managing chronic lung disorders, encompassing conditions such as pneumonia, tuberculosis, and COVID-19. While it is crucial for disease localization and severity assessment, existing computer-aided diagnosis (CAD) systems primarily focus on classification tasks, often overlooking these aspects. Additionally, prevalent approaches rely on class activation or saliency maps, providing only a rough localization. This research endeavors to address these limitations by proposing a comprehensive multi-stage framework. Initially, the framework identifies relevant lung areas by filtering out extraneous regions. Subsequently, an advanced fuzzy-based ensemble approach is employed to categorize images into specific classes. In the final stage, the framework identifies infected areas and quantifies the extent of infection in COVID-19 cases, assigning severity scores ranging from 0 to 3 based on the infection's severity. Specifically, COVID-19 images are classified into distinct severity levels, such as mild, moderate, severe, and critical, determined by the modified RALE scoring system. The study utilizes publicly available datasets, surpassing previous state-of-the-art works. Incorporating lung segmentation into the proposed ensemble-based classification approach enhances the overall classification process. This solution can be a valuable alternative for clinicians and radiologists, serving as a secondary reader for chest X-rays, reducing reporting turnaround times, aiding clinical decision-making, and alleviating the workload on hospital staff.

Decoding antimicrobial resistance: unraveling molecular mechanisms and targeted strategies

Antimicrobial resistance poses a significant global health threat, necessitating innovative approaches for combatting it. This review explores various mechanisms of antimicrobial resistance observed in various strains of bacteria. We examine various strategies, including antimicrobial peptides (AMPs), novel antimicrobial materials, drug delivery systems, vaccines, antibody therapies, and non-traditional antibiotic treatments. Through a comprehensive literature review, the efficacy and challenges of these strategies are evaluated. Findings reveal the potential of AMPs in combating resistance due to their unique mechanisms and lower propensity for resistance development. Additionally, novel drug delivery systems, such as nanoparticles, show promise in enhancing antibiotic efficacy and overcoming resistance mechanisms. Vaccines and antibody therapies offer preventive measures, although challenges exist in their development. Non-traditional antibiotic treatments, including CRISPR-Cas systems, present alternative approaches to combat resistance. Overall, this review underscores the importance of multifaceted strategies and coordinated global efforts to address antimicrobial resistance effectively.

Chasmophyte associated stress tolerant bacteria confer drought resilience to chickpea through efficient nutrient mining and modulation of stress response

In the present study, ten (10) selected bacteria isolated from chasmophytic wild Chenopodium were evaluated for alleviation of drought stress in chickpea. All the bacterial cultures were potential P, K and Zn solubilizer. About 50% of the bacteria could produce Indole-3-acetic acid (IAA) and 1-aminocyclopropane-1-carboxylate (ACC) deaminase. The bacteria showed wide range of tolerance towards pH, salinity, temperature and osmotic stress. Bacillus paralicheniformis L38, Pseudomonas sp. LN75, Enterobacter hormachei subsp. xiangfengensis LJ89, B. paramycoides L17 and Micrococcus luteus LA9 significantly improved growth and nutrient (N, P, K, Fe and Zn) content in chickpea under water stress during a green house experiment conducted following a completely randomized design (CRD). Application of Microbacterium imperiale LJ10, B. stercoris LN74, Pseudomonas sp. LN75, B. paralicheniformis L38 and E. hormachei subsp. xiangfengensis LJ89 reduced the antioxidant enzymes under water stress. During field experiments conducted following randomized block design (RBD), all the bacterial inoculations improved chickpea yield under water stress. Highest yield (1363 kg ha-1) was obtained in plants inoculated with Pseudomonas sp. LN75. Pseudomonas sp. LN75, B. paralicheniformis L38 and E. hormachei subsp. xiangfengensis LJ89 have potential as microbial stimulants to alleviate the water stress in chickpea. To the best of our knowledge this is the first report of using chasmophyte associated bacteria for alleviation of water stress in a crop plant.

Facile one-pot synthesis of the mesoporous chitosan-coated cobalt ferrite nanozyme as an antibacterial and MRI contrast agent

Cobalt ferrite (CoFe) nanoparticles (NPs) with appropriate physicochemical and biological properties have attracted great attention for biomedical applications. In the present study, chitosan-coated mesoporous CoFe (CoFeCH) NPs were synthesized using a facile one-step hydrothermal method and fully characterized using FE-SEM, EDS, BET, FTIR spectroscopy, DLS, TGA, XRD, and VSM. The spherical, highly colloidal, and monodispersed CoFeCH NPs with an average hydrodynamic size of 177.9 nm, PDI of 0.238 and zeta potential value of -33 represented a high saturation magnetization value of 59.37 emu g-1. N2 adsorption-desorption analysis confirmed the mesoporous structure of CoFeCH NPs with a type IV isotherm, calculated specific surface area of 89.583 m2 g-1 and total pore volume of 0.3668 cm3 g-1. CoFeCH NPs exhibited high antibacterial effects on S. aureus and E. coli, comparable with standard antibiotics, while CH-coating led to higher biocompatibility of CoFe NPs on human cells in vitro. CoFeCH NPs also showed significant peroxidase activity with a Km value of 14.37 and specific activity of 0.632 mmol min-1. CoFeCH NPs were successfully used as a MRI contrast agent with an R2 value of 91.3 mM-1 s-1. The overall results indicated the high potential of synthesized CoFeCH NPs by the present method for biomedical applications, especially as an antibacterial and MRI contrast agent.

Sustainable methods for the carboxymethylation and methylation of ursolic acid with dimethyl carbonate under mild and acidic conditions

Ursolic acid is a triterpene plant extract that exhibits significant potential as an anti-cancer, anti-tumour, and anti-inflammatory agent. Its direct use in the pharmaceutical industry is hampered by poor uptake of ursolic acid in the human body coupled with rapid metabolism causing a decrease in bioactivity. Modification of ursolic acid can overcome such issues, however, use of toxic reagents, unsustainable synthetic routes and poor reaction metrics have limited its potential. Herein, we demonstrate the first reported carboxymethylation and/or methylation of ursolic acid with dimethyl carbonate (DMC) as a green solvent and sustainable reagent under acidic conditions. The reaction of DMC with ursolic acid, in the presence of PTSA, ZnCl2, or H2SO4-SiO2 yielded the carboxymethylation product 3β-[[methoxy]carbonyl]oxyurs-12-en-28-oic acid, the methylation product 3β-methoxyurs-12-en-28-oic acid and the dehydration product urs-2,12-dien-28-oic acid. PTSA demonstrated high conversion and selectivity towards the previously unreported carboxymethylation of ursolic acid, while the application of formic acid in the system led to formylation of ursolic acid (3β-formylurs-12-en-28-oic acid) in quantitative yields via esterification, with DMC acting solely as a solvent. Meanwhile, the methylation product of ursolic acid, 3β-methoxyurs-12-en-28-oic acid, was successfully synthesised with FeCl3, demonstrating exceptional conversion and selectivity, >99% and 99%, respectively. Confirmed with the use of qualitative and quantitative green metrics, this result represents a significant improvement in conversion, selectivity, safety, and sustainability over previously reported methods of ursolic acid modification. It was demonstrated that these methods could be applied to other triterpenoids, including corosolic acid. The study also explored the potential pharmaceutical applications of ursolic acid, corosolic acid, and their derivatives, particularly in anti-inflammatory, anti-cancer, and anti-tumour treatments, using molecular ADMET and docking methods. The methods developed in this work have led to the synthesis of novel molecules, thus creating opportunities for the future investigation of biological activity and the modification of a wide range of triterpenoids applying acidic DMC systems to deliver novel active pharmaceutical intermediates.

Green synthesis of ZnO-TiO2/RGO nanocomposites using Senna surattensis extract: a novel approach for enhanced anticancer efficacy and biocompatibility

The purpose of the present study is to enhance the anticancer and biocompatibility performance of TiO2 NPs, ZnO NPs, ZnO-TiO2 (NCs), and ZnO-TiO2/reduced graphene oxide (RGO) NCs against two types of human cancer (HCT116) and normal (HUVCE) cells. A novel procedure for synthesizing ZnO-TiO2/RGO NCs has been developed using Senna surattensis extract. The improved physicochemical properties of the obtained samples were investigated using different techniques such as XRD, TEM, SEM, XPS, FTIR, DLS and UV-visible spectroscopy. XRD results showed that the addition of ZnO and RGO sheets affects the crystal structure and phase of TiO2 NPs. SEM and TEM images displayed that the TiO2 NPs and ZnO NPs were small with uniform spherical morphology in the prepared ZnO-TiO2/RGO NCs. Besides, it is shown that ZnO-TiO2 NCs anchored onto the surface of RGO sheets with a particle size of 14.80 ± 0.5 nm. XPS data confirmed the surface chemical composition and oxidation states of ZnO-TiO2/RGO NCs. Functional groups of prepared NPs and NCs were determined using FTIR spectroscopy. DLS data confirmed that the addition of ZnO and RGO sheets improves the negative surface charge of the prepared pure TiO2 NPs (-22.51 mV), ZnO NPs (-18.27 mV), ZnO-TiO2 NCs (-30.20 mV), and ZnO-TiO2/RGO NCs (-33.77 mV). Optical analysis exhibited that the bandgap energies of TiO2 NPs (3.30 eV), ZnO NPs (3.33 eV), ZnO-TiO2 NCs (3.03 eV), and ZnO-TiO2/RGO NCs (2.78 eV) were further enhanced by adding ZnO NPs and RGO sheets. This indicates that the synthesized samples can be applied to cancer therapy and environmental remediation. The biological data demonstrated that the produced ZnO-TiO2/RGO NCs show a more cytotoxic effect on HCT116 cells compared to pure TiO2 NPs and ZnO-TiO2 NCs. On the other hand, these NCs displayed the lowest level of toxicity towards normal HUVCE cells. These results indicate that the ZnO-TiO2/RGO NCs have strong toxicity against HCT116 cells and are compatible with normal cells. Our results show that the plant extract enhanced the physicochemical properties of NPs and NCs compared with the traditional chemical methods for synthesis. This study could open new avenues for developing more effective and targeted cancer treatments.

COVID‑19 detection from chest X-ray images using transfer learning

COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification.

Transparent Recombination Layers Design and Rational Characterizations for Efficient Two-Terminal Perovskite-Based Tandem Solar Cells

Two-terminal (2T) perovskite-based tandem solar cells (TSCs) arouse burgeoning interest in breaking the Shockley-Queisser (S-Q) limit of single-junction solar cells by combining two subcells with different bandgaps. However, the highest certified efficiency of 2T perovskite-based TSCs (33.9%) lags behind the theoretical limit (42-43%). A vital challenge limiting the development of 2T perovskite-based TSCs is the transparent recombination layers/interconnecting layers (RLs) design between two subcells. To improve the performance of 2T perovskite-based TSCs, RLs simultaneously fulfill the optical loss, contact resistance, carrier mobility, stress management, and conformal coverage requirements. In this review, the definition, functions, and requirements of RLs in 2T perovskite-based TSCs are presented. The insightful characterization methods applicable to RLs, which are inspiring for further research on the RLs both in 2T perovskite-based two-junction and multi-junction TSCs, are also highlighted. Finally, the key factors that currently limit the performance enhancement of RLs and the future directions that should be continuously focused on are summarized.

HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data

Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.

Combination of proton- or X-irradiation with anti-PDL1 immunotherapy in two murine oral cancers

Combining radiation therapy with immunotherapy is a strategy to improve both treatments. The purpose of this study was to compare responses for two syngeneic head and neck cancer (HNC) tumor models in mice following X-ray or proton irradiation with or without immune checkpoint inhibition (ICI). MOC1 (immunogenic) and MOC2 (less immunogenic) tumors were inoculated in the right hind leg of each mouse (C57BL/6J, n = 398). Mice were injected with anti-PDL1 (10 mg/kg, twice weekly for 2 weeks), and tumors were treated with single-dose irradiation (5-30 Gy) with X-rays or protons. MOC2 tumors grew faster and were more radioresistant than MOC1 tumors, and all mice with MOC2 tumors developed metastases. Irradiation reduced the tumor volume in a dose-dependent manner. ICI alone reduced the tumor volume for MOC1 with 20% compared to controls, while no reduction was seen for MOC2. For MOC1, there was a clear treatment synergy when combining irradiation with ICI for radiation doses above 5 Gy and there was a tendency for X-rays being slightly more biologically effective compared to protons. For MOC2, there was a tendency of protons being more effective than X-rays, but both radiation types showed a small synergy when combined with ICI. Although the responses and magnitudes of the therapeutic effect varied, the optimal radiation dose for maximal synergy appeared to be in the order of 10-15 Gy, regardless of tumor model.

Mechanical metamaterials as broadband electromagnetic wave absorbers: investigating relationships between geometrical parameters and electromagnetic response

The utilization of low-density and robust mechanical metamaterials rises as a promising solution for multifunctional electromagnetic wave absorbers due to their structured porous structures, which facilitates impedance matching and structural absorption. However, the various geometrical parameters involved in constructing these metamaterials affect their electromagnetic response, necessitating a comprehensive understanding of underlying absorbing mechanisms. Through experimentally validated numerical analysis, this study delves into the influence of geometrical factors on the electromagnetic response of representative low-density, high strength mechanical metamaterials, namely octet-truss and octet-foam. By juxtaposing electromagnetic response under varying volume fractions, cell lengths, and multilayer configurations of octet-truss and octet-foam, distinct absorption mechanisms emerge as geometrical parameters evolve. These mechanisms encompass diminished reflection owing to porous structures, effective medium approximations within subwavelength limits, and transmission-driven or reflection-driven phenomena originating from the interplay of open- and closed-cell structures. Through analyses on these mechanical metamaterials, we demonstrate the viability of employing them as tunable yet scalable structures that are lightweight, robust, and broadband electromagnetic wave absorption.

Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

A privacy-preserving publicly verifiable quantum random number generator

Verifying the quality of a random number generator involves performing computationally intensive statistical tests on large data sets commonly in the range of gigabytes. Limitations on computing power can restrict an end-user's ability to perform such verification. There are also random number-based applications where an honest user needs to publicly demonstrate that the random bits they are using pass the statistical tests without the bits being revealed. Here, we report the implementation of an entanglement-based protocol that allows a third party to publicly perform statistical tests without compromising the privacy of the random bits.

Epigenomic and transcriptomic persistence of heat stress memory in strawberry (Fragaria vesca)

BACKGROUND

In plants, epigenetic stress memory has so far been found to be largely transient. Here, we wanted to assess the heritability of heat stress-induced epigenetic and transcriptomic changes following woodland strawberry (Fragaria vesca) reproduction. Strawberry is an ideal model to study epigenetic inheritance because it presents two modes of reproduction: sexual (self-pollinated plants) and asexual (clonally propagated plants named daughter plants). Taking advantage of this model, we investigated whether heat stress-induced DNA methylation changes can be transmitted via asexual reproduction.

RESULTS

Our genome-wide study provides evidence for stress memory acquisition and maintenance in F. vesca. We found that specific DNA methylation marks or epimutations are stably transmitted over at least three asexual generations. Some of the epimutations were associated with transcriptional changes after heat stress.

CONCLUSION

Our findings show that the strawberry methylome and transcriptome respond with a high level of flexibility to heat stress. Notably, independent plants acquired the same epimutations and those were inherited by their asexual progenies. Overall, the asexual progenies can retain some information in the genome of past stresses encountered by their progenitors. This molecular memory, also documented at the transcriptional level, might be involved in functional plasticity and stress adaptation. Finally, these findings may contribute to novel breeding approaches for climate-ready plants.

Effects of common co-occurring pesticides (a neonicotinoid and fungicide) on honey bee colony health in a semi-field study

Multiple stressors are linked to declines of insects and important pollinators, such as bees. Recently, interactive effects of multiple agrochemicals on bees have been highlighted, including fungicides, which increase toxicity of neonicotinoid insecticides. Here, we use a semi-field study across two seasons in controlled foraging tunnels to test the effects of a field application of a commercial fungicide product with two active ingredients (pyraclostrobin and metconazole) applied at label rates. We also examine its interactive effects with the neonicotinoid insecticide clothianidin, at a conservative field-realistic dose of 2.23 ppb, on 48 honey bee colonies. We found combined effects of pesticide exposure, including additive 2.93-fold increases in mortality, and an additional effect of increased infestation levels of the ectoparasitic mite, Varroa destructor. Pesticide treatments also reduced colony activity, reduced colony weight, and increased sugar consumption of whole colonies. These findings indicate that typical sublethal exposure levels to common, co-occurring agrochemicals in the field significantly affect the health of whole honey bee colonies, highlighting an unintended consequence of increasing pesticide applications.

MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions

Synchrotron X-ray techniques are essential for studies of the intrinsic relationship between synthesis, structure, and properties of materials. Modern synchrotrons can produce up to 1 petabyte of data per day. Such amounts of data can speed up materials development, but also comes with a staggering growth in workload, as the data generated must be stored and analyzed. We present an approach for quickly identifying an atomic structure model from pair distribution function (PDF) data from (nano)crystalline materials. Our model, MLstructureMining, uses a tree-based machine learning (ML) classifier. MLstructureMining has been trained to classify chemical structures from a PDF and gives a top-3 accuracy of 99% on simulated PDFs not seen during training, with a total of 6062 possible classes. We also demonstrate that MLstructureMining can identify the chemical structure from experimental PDFs from nanoparticles of CoFe2O4 and CeO2, and we show how it can be used to treat an in situ PDF series collected during Bi2Fe4O9 formation. Additionally, we show how MLstructureMining can be used in combination with the well-known methods, principal component analysis (PCA) and non-negative matrix factorization (NMF) to analyze data from in situ experiments. MLstructureMining thus allows for real-time structure characterization by screening vast quantities of crystallographic information files in seconds.

The potential of thermally expanded graphite in oil sorption applications

An oil spill occurs when liquid petroleum hydrocarbons are released into the environment, whether accidentally or intentionally, in substantial quantities. The impact of an oil spill on the ecosystem is significant and should not be underestimated. Various techniques are employed to address oil spills, including mechanical, physical, biological, and physicochemical methods. Among these techniques, adsorption is considered the most suitable approach. Adsorption is promising due to its simplicity, ease of use, high removal capacity, and rapid pollutant removal. An excellent adsorbent material exhibits unique characteristics that enhance its efficacy in liquid adsorption. Sorbents are categorized into synthetic and natural types. Porous carbon materials, especially expanded graphite, are widely utilized in wastewater treatment due to their micropores and exceptional adsorption capacity. The distinctive properties of expanded graphite, including its low density, high porosity, and electrical conductivity, have garnered significant global attention for various potential applications. In essence, expanded graphite offers a powerful and practical approach to oil spill cleanup due to its efficient oil adsorption, selective targeting, ease of use, and potential reusability. This review article summarizes the preparation techniques, structure, and properties of expanded graphite. It also delves into recent advancements in using expanded graphite for oil spill cleanup. The article concludes by outlining potential future directions in this field and discussing the commercial viability of some of these techniques.

Development of a sustainable procedure for smartphone-based colorimetric determination of benzalkonium chloride in pharmaceutical preparations

A sustainable procedure offering green, simple, and rapid analysis was developed to determine benzalkonium chloride (BKC) in pharmaceutical preparations. The determination using smartphones was based on the ion pair colorimetric reaction with bromothymol blue (BTB), which produces a yellow color. The intensity of the product color, which is proportional to the concentration of BKC, was detected and evaluated using a smartphone camera and an image processing application. The procedure was performed in a microliter and was rapidly detected within 1 min after incubation. This offered high throughput at 28 samples per well plate in duplicate. Linear calibration, which was a plot of BKC concentrations and relative red intensities, was in the range of 2.0-24.0 μg/mL with an R2 of 0.997. The limits of detection (LOD) and quantitation (LOQ) were 1.0 and 3.2 μg/mL, respectively. This work was successful in applying it to pharmaceutical materials, disinfectant products, and pharmaceutical products containing BKC. It was discovered that the concentrations of BKC as an active ingredient in pharmaceutical materials were 82% w/v, whereas those in disinfectant products ranged from 0.4 to 2.1% w/v. In pharmaceutical products, ophthalmic drops and nasal sprays contain BKC as preservatives in the 0.01-0.02, and the 0.02% w/v, respectively. The results obtained by the proposed procedure compared with a reference titration method showed no significant differences at a 95% confidence level with 1.2-3.4% RSDs. This promotes the efficiency of pharmaceutical preparations regarding infection prevention and control by ensuring that available disinfectants contain a sufficient concentration of BKC. Additionally, this improves the efficiency of pharmaceutical preparations for quality control of pharmaceutical products by ensuring that the available preservatives maintain a sufficient concentration throughout the lifespan of the products.

Cryptic diversity in southern African kelp

The southern coast of Africa is one of the few places in the world where water temperatures are predicted to cool in the future. This endemism-rich coastline is home to two sister species of kelps of the genus Ecklonia maxima and Ecklonia radiata, each associated with specific thermal niches, and occuring primarily on opposite sides of the southern tip of Africa. Historical distribution records indicate that E. maxima has recently shifted its distribution ~ 70 km eastward, to sites where only E. radiata was previously reported. The contact of sister species with contrasting thermal affinities and the occurrence of mixed morphologies raised the hypothesis that hybridization might be occurring in this contact zone. Here we describe the genetic structure of the genus Ecklonia along the southern coast of Africa and investigate potential hybridization and cryptic diversity using a combination of nuclear microsatellites and mitochondrial markers. We found that both species have geographically discrete genetic clusters, consistent with expected phylogeographic breaks along this coastline. In addition, depth-isolated populations were found to harbor unique genetic diversity, including a third Ecklonia lineage. Mito-nuclear discordance and high genetic divergence in the contact zones suggest multiple hybridization events between Ecklonia species. Discordance between morphological and molecular identification suggests the potential influence of abiotic factors leading to convergent phenotypes in the contact zones. Our results highlight an example of cryptic diversity and hybridization driven by contact between two closely related keystone species with contrasting thermal affinities.