Unsupervised machine learning for risk stratification and identification of relevant subgroups of ascending aorta dimensions using cardiac CT and clinical data

The potential of precision population health lies in its capacity to utilize robust patient data for customized prevention and care targeted at specific groups. Machine learning has the potential to automatically identify clinically relevant subgroups of individuals, considering heterogeneous data sources. This study aimed to assess whether unsupervised machine learning (UML) techniques could interpret different clinical data to uncover clinically significant subgroups of patients suspected of coronary artery disease and identify different ranges of aorta dimensions in the different identified subgroups. We employed a random forest-based cluster analysis, utilizing 14 variables from 1170 (717 men/453 women) participants. The unsupervised clustering approach successfully identified four distinct subgroups of individuals with specific clinical characteristics, and this allows us to interpret and assess different ranges of aorta dimensions for each cluster. By employing flexible UML algorithms, we can effectively process heterogeneous patient data and gain deeper insights into clinical interpretation and risk assessment.

Microbes identified from monitoring cell manipulations in 5-year life of the Cell Factory G. Gaslini

Introduction

Quality and safety of a cell product, essential to guarantee the health of patients, depends on many factors including an appropriate environmental monitoring of the manufacturing rooms. Nonetheless, the maintenance of a controlled environment is requested to minimize the risk of contamination. Thus, a timely detection of changes in microbiological trends is important to adopt promptly effective measures against resistant strains that, in turn, may invalidate not only the sanitization procedures but also the safety of the cell product.

Methods

We analyzed microbes found in our cell processing clean room over the last 5 years. We used 10.147 plates for air sampler, passive air monitoring and for checking instruments and operators of the production unit.

Results

From these plates, 747 colonies were subjected to identification by the MALDI-TOF Vitek® MS system and the large majority of them was gram positive (97.8%) as witnessed by the finding that the most represented genera harvested from the classified areas were Staphylococcus (65%), Micrococcus (13%), Kocuria (8%) and Bacillus (5%). We never detected fungi. Most microbes found in the operators (both from class A and B) were collected from forearms and resulted of the Staphylococcus genus.

Conclusions

The observed microbial contamination is to be attributed to the personnel and no substantial microbial pitfalls in our Cell Factory has been detected.

Impact of an irreversible β-galactosylceramidase inhibitor on the lipid profile of zebrafish embryos

Krabbe disease is a sphingolipidosis characterized by the genetic deficiency of the acid hydrolase β-galactosylceramidase (GALC). Most of the studies concerning the biological role of GALC performed on Krabbe patients and Galc-deficient twitcher mice (an authentic animal model of the disease) indicate that the pathogenesis of this disorder is the consequence of the accumulation of the neurotoxic GALC substrate β-galactosylsphingosine (psychosine), ignoring the possibility that this enzyme may exert a wider biological impact. Indeed, limited information is available about the effect of GALC downregulation on the cell lipidome in adult and developing organisms. The teleost zebrafish (Danio rerio) has emerged as a useful platform to model human genetic diseases, including sphingolipidoses, and two GALC co-orthologs have been identified in zebrafish (galca and galcb). Here, we investigated the effect of the competitive and irreversible GALC inhibitor β-galactose-cyclophellitol (GCP) on the lipid profile of zebrafish embryos. Molecular modelling indicates that GCP can be sequestered in the catalytic site of the enzyme and covalently binds human GALC, and the zebrafish Galca and Galcb proteins in a similar manner. Accordingly, GCP inhibits the β-galactosylceramide hydrolase activity of zebrafish in vitro and in vivo, leading to significant alterations of the lipidome of zebrafish embryos. These results indicate that the lack of GALC activity deeply affects the lipidome during the early stages of embryonic development, and thereby provide insights into the pathogenesis of Krabbe disease.

Structure-guided identification and characterization of potent inhibitors targeting PhoP and MtrA to combat mycobacteria

Mycobacteria are causative agents of tuberculosis (TB), which is a global health concern. Drug-resistant TB strains are rapidly emerging, thereby necessitating the urgent development of new drugs. Two-component signal transduction systems (TCSs) are signaling pathways involved in the regulation of various bacterial behaviors and responses to environmental stimuli. Applying specific inhibitors of TCSs can disrupt bacterial signaling, growth, and virulence, and can help combat drug-resistant TB. We conducted a comprehensive pharmacophore-based inhibitor screening and biochemical and biophysical examinations to identify, characterize, and validate potential inhibitors targeting the response regulators PhoP and MtrA of mycobacteria. The constructed pharmacophore model Phar-PR-n4 identified effective inhibitors of formation of the PhoP-DNA complex: ST132 (IC50 = 29 ± 1.6 µM) and ST166 (IC50 = 18 ± 1.3 µM). ST166 (KD = 18.4 ± 4.3 μM) and ST132 (KD = 14.5 ± 0.1 μM) strongly targeted PhoP in a slow-on, slow-off manner. The inhibitory potency and binding affinity of ST166 and ST132 for MtrAC were comparable to those of PhoP. Structural analyses and molecular dynamics simulations revealed that ST166 and ST132 mainly interact with the α8-helix and C-terminal β-hairpin of PhoP, with functionally essential residue hotspots for structure-based inhibitor optimization. Moreover, ST166 has in vitro antibacterial activity against Macrobacterium marinum. Thus, ST166, with its characteristic 1,2,5,6-tetrathiocane and terminal sulphonic groups, has excellent potential as a candidate for the development of novel antimicrobial agents to combat pathogenic mycobacteria.

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.

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature

RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.

Co-expression of immune checkpoints in glioblastoma revealed by single-nucleus RNA sequencing and spatial transcriptomics

Glioblastoma (GBM) is one of the most malignant tumors of the central nervous system. The pattern of immune checkpoint expression in GBM remains largely unknown. We performed snRNA-Seq and spatial transcriptomic (ST) analyses on untreated GBM samples. 8 major cell types were found in both tumor and adjacent normal tissues, with variations in infiltration grade. Neoplastic cells_6 was identified in malignant cells with high expression of invasion and proliferator-related genes, and analyzed its interactions with microglia, MDM cells and T cells. Significant alterations in ligand-receptor interactions were observed, particularly between Neoplastic cells_6 and microglia, and found prominent expression of VISTA/VSIG3, suggesting a potential mechanism for evading immune system attacks. High expression of TIM-3, VISTA, PSGL-1 and VSIG-3 with similar expression patterns in GBM, may have potential as therapeutic targets. The prognostic value of VISTA expression was cross-validated in 180 glioma patients, and it was observed that patients with high VISTA expression had a poorer prognosis. In addition, multimodal cross analysis integrated SnRNA-seq and ST, revealing complex intracellular communication and mapping the GBM tumor microenvironment. This study reveals novel molecular characteristics of GBM, co-expression of immune checkpoints, and potential therapeutic targets, contributing to improving the understanding and treatment of GBM.

Techniques for assessing telomere length: A methodological review

Telomeres are located at the ends of chromosomes and have specific sequences with a distinctive structure that safeguards genes. They possess capping structures that protect chromosome ends from fusion events and ensure chromosome stability. Telomeres shorten in length during each cycle of cell division. When this length reaches a certain threshold, it can lead to genomic instability, thus being implicated in various diseases, including cancer and neurodegenerative disorders. The possibility of telomeres serving as a biomarker for aging and age-related disease is being explored, and their significance is still under study. This is because post-mitotic cells, which are mature cells that do not undergo mitosis, do not experience telomere shortening due to age. Instead, other causes, for example, exposure to oxidative stress, can directly damage the telomeres, causing genomic instability. Nonetheless, a general agreement has been established that measuring telomere length offers valuable insights and forms a crucial foundation for analyzing gene expression and epigenetic data. Numerous approaches have been developed to accurately measure telomere lengths. In this review, we summarize various methods and their advantages and limitations for assessing telomere length.

Large language models assisted multi-effect variants mining on cerebral cavernous malformation familial whole genome sequencing

Cerebral cavernous malformation (CCM) is a polygenic disease with intricate genetic interactions contributing to quantitative pathogenesis across multiple factors. The principal pathogenic genes of CCM, specifically KRIT1, CCM2, and PDCD10, have been reported, accompanied by a growing wealth of genetic data related to mutations. Furthermore, numerous other molecules associated with CCM have been unearthed. However, tackling such massive volumes of unstructured data remains challenging until the advent of advanced large language models. In this study, we developed an automated analytical pipeline specialized in single nucleotide variants (SNVs) related biomedical text analysis called BRLM. To facilitate this, BioBERT was employed to vectorize the rich information of SNVs, while a deep residue network was used to discriminate the classes of the SNVs. BRLM was initially constructed on mutations from 12 different types of TCGA cancers, achieving an accuracy exceeding 99%. It was further examined for CCM mutations in familial sequencing data analysis, highlighting an upstream master regulator gene fibroblast growth factor 1 (FGF1). With multi-omics characterization and validation in biological function, FGF1 demonstrated to play a significant role in the development of CCMs, which proved the effectiveness of our model. The BRLM web server is available at http://1.117.230.196.

Computational design of α-amylase from Bacillus licheniformis to increase its activity and stability at high temperatures

The thermostable α-amylase derived from Bacillus licheniformis (BLA) has multiple advantages, including enhancing the mass transfer rate and by reducing microbial contamination in starch hydrolysis. Nonetheless, the application of BLA is constrained by the accessibility and stability of enzymes capable of achieving high conversion rates at elevated temperatures. Moreover, the thermotolerance of BLA requires further enhancement. Here, we developed a computational strategy for constructing small and smart mutant libraries to identify variants with enhanced thermostability. Initially, molecular dynamics (MD) simulations were employed to identify the regions with high flexibility. Subsequently, FoldX, a computational design predictor, was used to design mutants by rigidifying highly flexible residues, whereas the simultaneous decrease in folding free energy assisted in improving thermostability. Through the utilization of MD and FoldX, residues K251, T277, N278, K319, and E336, situated at a distance of 5 Å from the catalytic triad, were chosen for mutation. Seventeen mutants were identified and characterized by evaluating enzymatic characteristics and kinetic parameters. The catalytic efficiency of the E271L/N278K mutant reached 184.1 g L-1 s-1, which is 1.88-fold larger than the corresponding value determined for the WT. Furthermore, the most thermostable mutant, E336S, exhibited a 1.43-fold improvement in half-life at 95 ℃, compared with that of the WT. This study, by combining computational simulation with experimental verification, establishes that potential sites can be computationally predicted to increase the activity and stability of BLA and thus provide a possible strategy by which to guide protein design.

Adjunctive aids for the detection of oral squamous cell carcinoma and oral potentially malignant disorders: A systematic review of systematic reviews

This study presents the results of systematic reviews on adjunctive tools in screening and diagnosis of oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMD) and to determine if the current literature supports their use as either an adjunctive tool or replacement of gold standard techniques. Systemic reviews and meta-analysis that evaluated adjunctive tools including chemiluminescence, tissue autofluorescence, tissue fluorescence spectroscopy, vital staining and cytology techniques were systematically examined using AMSTAR II. Twenty-seven systematic reviews were included. Five studies had a low quality of evidence, and nine studies had a critically low quality of evidence. This review found limited evidence to recommend chemiluminescence, tissue autofluorescence tools and vital staining as diagnostic tools, but only serve as clinical adjuncts to conventional oral examination. Cytology techniques and narrow-band imaging may be utilised as a non-invasive diagnostic adjunctive tool for the detection of OSCC and the malignant transformation of OPMD. In conclusion, this paper provides evidence on several types of adjunctive tools and provides recommendations on their use in clinical practice. These tools are considered useful as clinical adjuncts but there is insufficient evidence for their use as a diagnostic tool to replace gold standard conventional oral examination and surgical biopsy.

GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer

Gastric cancer (GC) is one of the most commonly diagnosed malignancies, threatening millions of lives worldwide each year. Importantly, GC is a heterogeneous disease, posing a significant challenge to the selection of patients for more optimized therapy. Over the last decades, extensive community effort has been spent on dissecting the heterogeneity of GC, leading to the identification of distinct molecular subtypes that are clinically relevant. However, so far, no tool is publicly available for GC subtype prediction, hindering the research into GC subtype-specific biological mechanisms, the design of novel targeted agents, and potential clinical applications. To address the unmet need, we developed an R package GCclassifier for predicting GC molecular subtypes based on gene expression profiles. To facilitate the use by non-bioinformaticians, we also provide an interactive, user-friendly web server implementing the major functionalities of GCclassifier. The predictive performance of GCclassifier was demonstrated using case studies on multiple independent datasets.

Respiratory tract barrier dysfunction in viral-bacterial co-infection cases

A preceding viral infection of the respiratory tract predisposes the host to secondary bacterial pneumonia, known as a major cause of morbidity and mortality. However, the underlying mechanism of the viral-bacterial synergy that leads to disease progression has remained elusive, thus hampering the production of effective prophylactic and therapeutic intervention options. In addition to viral-induced airway epithelial damage, which allows dissemination of bacteria to the lower respiratory tract and increases their invasiveness, dysfunction of immune defense following a viral infection has been implicated as a factor for enhanced susceptibility to secondary bacterial infections. Given the proximity of the oral cavity to the respiratory tract, where viruses enter and replicate, it is also well-established that oral health status can significantly influence the initiation, progression, and pathology of respiratory viral infections. This review was conducted to focus on the dysfunction of the respiratory barrier, which plays a crucial role in providing physical and secretory barriers as well as immune defense in the context of viral-bacterial synergy. Greater understanding of barrier response to viral-bacterial co-infections, will ultimately lead to development of effective, broad-spectrum therapeutic approaches for prevention of enhanced susceptibility to these pathogens.

PgxSAVy: A tool for comprehensive evaluation of variant peptide quality in proteogenomics - catching the (un)usual suspects

Variant peptides resulting from single nucleotide polymorphisms (SNPs) can lead to aberrant protein functions and have translational potential for disease diagnosis and personalized therapy. Variant peptides detected by proteogenomics are fraught with high number of false positives, but there is no uniform and comprehensive approach to assess variant quality across analysis pipelines. Despite class-specific FDR along with ad-hoc filters, the problem is far from solved. These protocols are typically manual and tedious, and thus not uniform across labs. We demonstrate that variant peptide rescoring, integrated with intensity, variant event information and search result features, allows better discrimination of correct variant peptides. Implemented into PgxSAVy - a tool for quality control of variant peptides, this method can tackle the high rate of false positives. PgxSAVy provides a rigorous framework for quality control and annotations of variant peptides on the basis of (i) variant quality, (ii) isobaric masses, and (iii) disease annotation. PgxSAVy demonstrated high accuracy by identifying true variants with 98.43% accuracy on simulated data. Large-scale proteogenomic reanalysis of ∼2.8 million spectra (PXD004010 and PXD001468) resulted in 12,705 variant peptide spectrum matches (PSMs), of which PgxSAVy evaluated 3028 (23.8%), 1409 (11.1%) and 8268 (65.1%) as confident, semi-confident and doubtful respectively. PgxSAVy also annotates the variants based on their pathogenicity and provides support for assisted manual validation. The analysis of proteins carrying variants can provide fine granularity in discovering important pathways. PgxSAVy will advance personalized medicine by providing a comprehensive framework for quality control and prioritization of proteogenomics variants. PgxSAVy is freely available at https://pgxsavy.igib.res.in/ as a webserver and https://github.com/anuragraj/PgxSAVy as a stand-alone tool.

Helix-to-sheet transition of the Aβ42 peptide revealed using an enhanced sampling strategy and Markov state model

The self-assembly of Aβ peptides into toxic oligomers and fibrils is the primary cause of Alzheimer's disease. Moreover, the conformational transition from helix to sheet is considered a crucial step in the aggregation of Aβ peptides. However, the structural details of this process still remain unclear due to the heterogeneity and transient nature of the Aβ peptides. In this study, we developed an enhanced sampling strategy that combines artificial neural networks (ANN) with metadynamics to explore the conformational space of the Aβ42 peptides. The strategy consists of two parts: applying ANN to optimize CVs and conducting metadynamics based on the resulting CVs to sample conformations. The results showed that this strategy achieved better sampling performance in terms of the distribution of sampled conformations. The sampling efficiency is increased by 10-fold compared to our previous Hamiltonian Exchange Molecular Dynamics (MD) and by 1000-fold compared to ordinary MD. Based on the sampled conformations, we constructed a Markov state model to understand the detailed transition process. The intermediate states in this process are identified, and the connecting paths are analyzed. The conformational transitions in D23-K28 and M35-V40 are proven to be crucial for aggregation. These results are helpful in clarifying the mechanism and process of Aβ42 peptide aggregation. D23-K28 and M35-V40 can be identified as potential targets for screening and designing inhibitors of Aβ peptide aggregation.

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.

Registry and survey of circadian rhythm sleep-wake disorder patients

Objective

Circadian Sleep Disorders Network has created a registry of circadian rhythm sleep-wake disorder (CRSWD) patients, and a survey of their experiences. The purpose of the registry is to provide volunteers willing to participate in research; the purpose of the survey is to fill some of the knowledge gaps on these disorders, including information on subjective patient experience and the efficacy and durability of treatments.Researchers are invited to contact Circadian Sleep Disorders Network for permission to use the registry to find potential research participants, and to further analyze the survey data.

Patients

Over 1627 patients have participated; 1298 have completed the entire survey. Here we present results based on the 479 clinically diagnosed CRSWD patients.

Methods

The survey covers a variety of topics relating to CRSWDs, including diagnosis, comorbidities, treatments, and work/educational accommodations.

Conclusions

Results of this survey diverged from much of the literature. More than half the participants reported tiredness even when sleeping on their preferred schedule. While depression may cause sleep problems, our data suggests that sleep/circadian problems often precede depression.There were more people suffering from sighted non-24-hour sleep-wake rhythm disorder than some of the literature would lead us to expect.Current treatments did not appear to be helpful to a large percentage of our participants. Most of them did not find light therapy helpful and nearly all participants who tried phase-delay chronotherapy reported at best only short-term improvement. A sizable proportion of people who tried phase-delay chronotherapy subsequently developed non-24-hour sleep-wake rhythm disorder.

Beyond the Scalpel: Assessing ChatGPT's potential as an auxiliary intelligent virtual assistant in oral surgery

AI has revolutionized the way we interact with technology. Noteworthy advances in AI algorithms and large language models (LLM) have led to the development of natural generative language (NGL) systems such as ChatGPT. Although these LLM can simulate human conversations and generate content in real time, they face challenges related to the topicality and accuracy of the information they generate. This study aimed to assess whether ChatGPT-4 could provide accurate and reliable answers to general dentists in the field of oral surgery, and thus explore its potential as an intelligent virtual assistant in clinical decision making in oral surgery. Thirty questions related to oral surgery were posed to ChatGPT4, each question repeated 30 times. Subsequently, a total of 900 responses were obtained. Two surgeons graded the answers according to the guidelines of the Spanish Society of Oral Surgery, using a three-point Likert scale (correct, partially correct/incomplete, and incorrect). Disagreements were arbitrated by an experienced oral surgeon, who provided the final grade Accuracy was found to be 71.7%, and consistency of the experts' grading across iterations, ranged from moderate to almost perfect. ChatGPT-4, with its potential capabilities, will inevitably be integrated into dental disciplines, including oral surgery. In the future, it could be considered as an auxiliary intelligent virtual assistant, though it would never replace oral surgery experts. Proper training and verified information by experts will remain vital to the implementation of the technology. More comprehensive research is needed to ensure the safe and successful application of AI in oral surgery.

PLO3S: Protein LOcal Surficial Similarity Screening

The study of protein molecular surfaces enables to better understand and predict protein interactions. Different methods have been developed in computer vision to compare surfaces that can be applied to protein molecular surfaces. The present work proposes a method using the Wave Kernel Signature: Protein LOcal Surficial Similarity Screening (PLO3S). The descriptor of the PLO3S method is a local surface shape descriptor projected on a unit sphere mapped onto a 2D plane and called Surface Wave Interpolated Maps (SWIM). PLO3S allows to rapidly compare protein surface shapes through local comparisons to filter large protein surfaces datasets in protein structures virtual screening protocols.

Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi

Machine learning models are increasingly used in the medical domain to study the association between risk factors and diseases to support practitioners in understanding health outcomes. In this paper, we showcase the use of machine-learned staged tree models for investigating complex asymmetric dependence structures in health data. Staged trees are a specific class of generative, probabilistic graphical models that formally model asymmetric conditional independence and non-regular sample spaces. An investigation of the risk factors in invasive fungal infections demonstrates the insights staged trees provide to support medical decision-making.

Research progress in stem cell therapy for Wilson disease

Wilson disease (WD), also known as hepatolenticular degeneration, is an autosomal recessive disorder characterized by disorganized copper metabolism caused by mutations in the ATP7B gene. Currently, the main treatment options for WD involve medications such as d-penicillamine, trientine hydrochloride, zinc acetate, and liver transplantation. However, there are challenges that encompass issues of poor compliance, adverse effects, and limited availability of liver sources that persist. Stem cell therapy for WD is currently a promising area of research. Due to the advancement in stem cell directed differentiation technology in vitro and the availability of sufficient stem cell donors, it is expected to be a potential treatment option for the permanent correction of abnormal copper metabolism. This article discusses the research progress of stem cell therapy for WD from various sources, as well as the challenges and future prospects of the clinical application of stem cell therapy for WD.

Diverting the food-freezing technology improves the cryopreservation efficiency of induced pluripotent stem cells and derived neurospheres

Introduction

Recent advances in induced pluripotent stem (iPS) technology and regenerative medicine require effective cryopreservation of iPSC-derived differentiated cells and three-dimensional cell aggregates (eg. Spheroids and organoids). Moreover, innovative freezing technologies for keeping food fresh over the long-term rapidly developed in the food industry. Therefore, we examined whether one of such freezing technologies, called "Dynamic Effect Powerful Antioxidation Keeping (DEPAK)," could be effective for the cryopreservation of biological materials.

Methods

We evaluated the efficiency of cryopreservation using DEPAK and Proton freezers, both of which are used in the food industry, compared with conventional slow-freezing methods using a programmable freezer and a cell-freezing vessel. As they are highly susceptible cells to freeze-thaw damage, we selected two suspension cell lines (KHYG-1 derived from human natural killer cell leukemia and THP-1 derived from human acute monocyte leukemia) and two adherent cell lines (OVMANA derived from human ovarian tumors and HuH-7 derived from human hepatocarcinoma). We used two human iPS cell lines, 201B7-Ff and 1231A3, which were either undifferentiated or differentiated into neurospheres. After freezing using the above methods, the frozen cells and neurospheres were immediately transferred to liquid nitrogen. After thawing, we assessed the cryopreservation efficiency of cell viability, proliferation, neurosphere formation, and neurite outgrowth after thawing.

Results

Among the four cryopreservation methods, DEPAK freezing resulted in the highest cell proliferation in suspension and adherent cell lines. Similar results were obtained for the cryopreservation of undifferentiated human iPS cells. In addition, we demonstrated that the DEPAK freezing method sustained the neurosphere formation capacity of differentiated iPS cells to the same extent as unfrozen controls. In addition, we observed that DEPAK-frozen neurospheres exhibited higher viability after thawing and underwent neural differentiation more efficiently than slow-freezing methods.

Conclusions

Our results suggest that diversifying food-freezing technologies can overcome the difficulties associated with the cryopreservation of various biological materials, including three-dimensional cell aggregates.

Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma

Objectives

Combination therapy of lenvatinib and immune checkpoint inhibitors (CLICI) has emerged as a promising approach for managing unresectable hepatocellular carcinoma (HCC). However, the response to such treatment is observed in only a subset of patients, underscoring the pressing need for reliable methods to identify potential responders.

Materials & methods

This was a retrospective analysis involving 120 patients with unresectable HCC. They were divided into training (n = 72) and validation (n = 48) cohorts. We developed an interpretable deep learning model using multiphase computed tomography (CT) images to predict whether patients will respond or not to CLICI treatment, based on the Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1). We evaluated the models' performance and analyzed the impact of each CT phase. Critical regions influencing predictions were identified and visualized through heatmaps.

Results

The multiphase model outperformed the best biphase and uniphase models, achieving an area under the curve (AUC) of 0.802 (95% CI = 0.780-0.824). The portal phase images were found to significantly enhance the model's predictive accuracy. Heatmaps identified six critical features influencing treatment response, offering valuable insights to clinicians. Additionally, we have made this model accessible via a web server at http://uhccnet.com/ for ease of use.

Conclusions

The integration of multiphase CT images with deep learning-generated heatmaps for predicting treatment response provides a robust and practical tool for guiding CLICI therapy in patients with unresectable HCC.

On secondary structure avoidance of codes for DNA storage

A secondary structure in single-stranded DNA refers to its propensity to undergo self-folding, leading to functional inactivity and irreparable failures within DNA storage systems. Consequently, the property of secondary structure avoidance (SSA) becomes a crucial criterion in the design of single-stranded DNA sequences for DNA storage, as it prohibits the inclusion of reverse-complement subsequences that contribute to such structures. This work is specifically focused on addressing the avoidance of secondary structures in single-stranded DNA sequences. We propose a novel sequence replacement approach, which successfully resolves the SSA problem under conditions where the stem exceeds a length of 2 log 2 ⁡ n + 2 , and the loop is of length k ≥ 4 . These parameters have been carefully chosen to closely resemble the real-world scenarios encountered in biochemical processes, enhancing the practical relevance of our study.

Artificial intelligence in skeletal metastasis imaging

In the field of metastatic skeletal oncology imaging, the role of artificial intelligence (AI) is becoming more prominent. Bone metastasis typically indicates the terminal stage of various malignant neoplasms. Once identified, it necessitates a comprehensive revision of the initial treatment regime, and palliative care is often the only resort. Given the gravity of the condition, the diagnosis of bone metastasis should be approached with utmost caution. AI techniques are being evaluated for their efficacy in a range of tasks within medical imaging, including object detection, disease classification, region segmentation, and prognosis prediction in medical imaging. These methods offer a standardized solution to the frequently subjective challenge of image interpretation.This subjectivity is most desirable in bone metastasis imaging. This review describes the basic imaging modalities of bone metastasis imaging, along with the recent developments and current applications of AI in the respective imaging studies. These concrete examples emphasize the importance of using computer-aided systems in the clinical setting. The review culminates with an examination of the current limitations and prospects of AI in the realm of bone metastasis imaging. To establish the credibility of AI in this domain, further research efforts are required to enhance the reproducibility and attain robust level of empirical support.

Combined analysis of cross-population healthy adult human microbiome reveals consistent differences in gut microbial characteristics between Western and non-Western countries

Despite extensive research on the gut microbiome of healthy individuals from a single country, there are still a limited number of population-level comparative studies. Moreover, the sequencing approach used in most related studies involves 16 S ribosomal RNA (rRNA) sequencing with a limited resolution, which cannot provide detailed functional profiles. In the present study, we applied a combined analysis approach to analyze whole metagenomic shotgun sequencing data from 2035 healthy adult samples from six countries across four continents. Analysis of core species revealed that 13 species were present in more than 90 % of all investigated individuals, the majority of which produced short-chain fatty acids (SCFA)-producing bacteria. Our analysis revealed consistently significant differences in gut microbial species and pathways between Western and non-Western countries, such as Escherichia coli and the relation of MetaCyc pathways to the TCA cycle. Specific changes in microbial species and pathways are potentially related to lifestyle and diet. Furthermore, we identified several noteworthy microbial species and pathways that exhibit distinct characteristics specific to China. Interestingly, we observed that China (CHN) was more similar to the United States (USA) and United Kingdom (GBR) in terms of the taxonomic and functional composition of the gut microbiome than India (IND) and Madagascar (MDG), which were more similar to the China (CHN) diet. The current study identified consistent microbial features associated with population and geography, which will inspire further clinical translations that consider paying attention to differences in microbiota backgrounds and confounding factors.

miR-21 Responsive Nanocarrier Targeting Ovarian Cancer Cells

In recent years, DNA origami-based nanocarriers have been extensively utilized for efficient cancer therapy. However, developing a nanocarrier capable of effectively protecting cargos such as RNA remains a challenge. In this study, we designed a compact and controllable DNA tubular origami (DTO) measuring 120 nm in length and 18 nm in width. The DTO exhibited appropriate structural characteristics for encapsulating and safeguarding cargo. Inside the DTO, we incorporated 20 connecting points to facilitate the delivery of cargoes to various ovarian and normal epithelial cell lines. Specifically, fluorescent-labeled DNA strands were attached to these sites as cargoes. The DTO was engineered to open upon encountering miR-21 through RNA/DNA strand displacement. Significantly, for the first time, we inhibited fluorescence using the compact DNA nanotube and observed dynamic fluorescent signals, indicating the controllable opening of DTO through live-cell imaging. Our results demonstrated that the DTO remained properly closed, exhibited effective internalization in ovarian cancer cells in vitro, showcasing marked differential expression of miR-21, and efficiently opened with short-term exposure to miR-21. Leveraging its autonomous behavior and compact design, the DTO emerges as a promising nanocarrier for various clinically relevant materials. It holds significant application prospects in anti-cancer therapy and the development of flexible biosensors.

The test-induced warm-up effect on hamstring flexibility tests

Background

Although the effect of active warm-up (WU) on acute flexibility enhancement is well documented, the test-induced WU effect in muscle length test has not been widely studied.

Objective

This study aimed to verify the test-induced WU effect on hamstring flexibility tests.

Methods

The active knee extension (AKE) was performed using the right leg, whereas the straight leg raise (SLR) was performed using the left leg. Ten trials of AKE or SLR were performed: two as the pre-intervention trials (Pre); six as the WU intervention; and another two trials as the post-intervention (Post). During WU, subjects in the WO-Hold group performed six trials of the AKE or SLR without hold, and those in the W-Hold group performed six trials of the AKE or SLR with a 5 s hold.

Results

A significant difference was noted between Pre-AKE and Post-AKE, and between Pre-SLR and Post-SLR, respectively, in both the groups. The effect of WU is clear when performing consecutive AKE or SLR without any additional hold.

Conclusion

Practitioners should be cautious in interpreting the testing result to avoid overestimation of the treatment effect since the test itself may induce substantial WU effect to the target tissues.

Augusta: From RNA-Seq to gene regulatory networks and Boolean models

Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.

DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements

Gene transcription is an essential process involved in all aspects of cellular functions with significant impact on biological traits and diseases. This process is tightly regulated by multiple elements that co-operate to jointly modulate the transcription levels of target genes. To decipher the complicated regulatory network, we present a novel multi-view attention-based deep neural network that models the relationship between genetic, epigenetic, and transcriptional patterns and identifies co-operative regulatory elements (COREs). We applied this new method, named DeepCORE, to predict transcriptomes in various tissues and cell lines, which outperformed the state-of-the-art algorithms. Furthermore, DeepCORE contains an interpreter that extracts the attention values embedded in the deep neural network, maps the attended regions to putative regulatory elements, and infers COREs based on correlated attentions. The identified COREs are significantly enriched with known promoters and enhancers. Novel regulatory elements discovered by DeepCORE showed epigenetic signatures consistent with the status of histone modification marks.

Adaptation of a health literacy screener for computerized, self-administered use by U.S. adults

Objective

Health literacy is a critical health determinant, for which few computerized, self-administered assessments exist. This study adapted and tested the reliability of the Newest Vital Sign© (NVS) as a computerized, self-administered health literacy screener.

Methods

Phase one involved 33 participants to create response options for a computerized, self-administered NVS (C-NVS). Phase two was a randomized crossover trial to test the consistency of C-NVS and original, interviewer-administered NVS (I-NVS) scores in 89 participants.

Results

Linear mixed-effects regression model results showed a significant carryover effect (p < .001). Crossover trial data from time 1 showed that participants who initially received the C-NVS had significantly higher average scores (M = 5.7, SD = 0.6) than participants who received the I-NVS (M = 4.5, SD = 1.5; t(87) = 5.25, p < .001). Exploratory analysis results showed that when the washout period was longer than 33 days (75th percentile) the carryover effect was not statistically significant (p = .077).

Conclusion and innovation

Findings suggest learning can occur when health literacy screeners are administered more than once in less than a month's time and computerized, self-administered health literacy screeners may produce ceiling effects. A universal precautions approach to health literacy therefore remains germane.

Digital health outreach to promote postpartum screening after gestational diabetes: A randomized factorial pilot study

Objective

We examined the acceptability and feasibility of a multi-component digital health outreach intervention to promote uptake of guideline-recommended postpartum screening for type 2 diabetes among patients with gestational diabetes (GDM).

Methods

We conducted a 24 randomized factorial experiment as part of the Multiphase Optimization Strategy (MOST) preparation phase for developing behavioral interventions. Participants with current or recent GDM in an integrated healthcare system were randomized to receive an outreach message with up to four intervention components, designed to be self-administered in about 10 min and efficiently delivered online via REDCap: a streamlined values affirmation, personalized information on diabetes risk, an interactive motivational interviewing-based component, and an interactive action planning component. Patient-reported acceptability and feasibility outcomes were assessed via survey.

Results

Among 162 participants, 72% self-identified with a racial/ethnic minority group. Across components, acceptability scores averaged 3.9/5; ≥91% of participants read most or all of the outreach message; ≥89% perceived the amount of information as "about right"; and ≥ 87% completed ≥1 interactive prompt.

Conclusion

Each intervention component was acceptable to diverse patients and feasible to deliver in a brief, self-directed, online format.

Innovation

These novel components target unaddressed barriers to patient engagement in guideline-recommended postpartum diabetes screening and adapt theory-based behavior change techniques for large-scale use.

Healthcare provider assessments of caregiver communication behaviors during gynecologic Cancer treatment appointments

Objective

Caregivers often accompany patients to cancer-related medical appointments. Limited research exists on healthcare providers' (HCPs) evaluation of how caregiver communication influences interactions between healthcare providers and patients, particularly during gynecologic treatment visits. HCPs may perceive caregiver communication as helpful or challenging, and these triadic interactions may influence patient outcomes.

Methods

Interviews with ten cancer specialist HCPs (medical assistants/technicians, nurse practitioners/registered nurses, oncologists) addressed experiences interacting with patients and caregivers.

Results

Analyses revealed two themes concerning helpful communication: caregivers managing information and managing patient emotions. Three challenging themes include caregiver communication unsettling healthcare interactions, caregiver presence limiting patient communication, and caregiver engagement challenges.

Conclusion

HCPs evaluate caregiver communication as helpful and challenging. Findings suggest benefits of communication training for gynecologic cancer patients such as requesting privacy when interacting with HCPs, for caregivers to promote awareness of effects of their behavior, and for HCPs to help manage triadic interactions while supporting patient needs.

Innovation

HCP assessment of caregiver communication during gynecologic treatment visits offers unique insights regarding helpful and challenging behaviors contributing to implications for patient care and well-being. Applications may extend to other triadic interactions and cancer settings.

Rapid cycle deliberate practice: application in forceps simulation training for gynecology and obstetrics residents

BACKGROUND

Rapid Cycle Deliberate Practice (RCDP) has gained prominence in recent years as an innovative teaching method in simulation-based training for adult and pediatric emergency medical skills. However, its application in the training of forceps delivery skills among obstetrics and gynecology residents remains unexplored. This study aimed to assess the impact of RCDP in this domain.

METHODS

Conducted in March 2021, this randomized controlled study involved 60 second-year obstetrics and gynecology residents undergoing standardized training. Participants were randomly assigned to the RCDP group or the traditional teaching method (TTM) group, each comprising 30 residents. The RCDP group followed the RCDP practice mode, while the TTM group adhered to conventional simulation teaching. Post-training assessment of operational proficiency was conducted immediately and after one year. Independent operational confidence and training satisfaction were evaluated through questionnaire surveys and the Satisfaction with Simulation Experience (SSE) scale. Data analysis utilized SPSS 23.0.

RESULTS

The RCDP group displayed significantly higher immediate post-training forceps operation scores compared to the TTM group (92.00 [range: 90.00-94.00] vs. 88.00 [range: 86.75-92.00]; z = 3.79; p < .001). However, no significant difference emerged in forceps operation scores after one year (86.00 [range: 85.00-88.00] vs. 85.50 [range: 84.00-88.25]; z = 0.54; p = .59). The RCDP group exhibited notable performance improvement over the TTM group (z = 3.49; p < .001). Independent operation confidence showed no significant discrepancy (p > .05). Importantly, the RCDP group reported higher satisfaction scores, particularly in the Debriefing and Reflection subscale (44.00 [range: 43.00-45.00] vs. 41.00 [range: 41.50-43.00]; z = 5.24; p < .001), contributing to an overall superior SSE score (z = 4.74; p < .001).

CONCLUSIONS

RCDP exhibits immediate efficacy in elevating forceps delivery skills among residents. However, sustained skill enhancement necessitates innovative approaches, while RCDP's value lies in tailored feedback and reflection for enriched medical education.

"Using behavioral insights to inform the COVID-19 vaccine response in Kosovo[1]: Population perceptions and interventions"

Objective

Understand population perceptions in Kosovo[1] regarding COVID-19 vaccination to inform the pandemic response.

Methods

Five rounds of a cross-sectional survey in representative samples of adults during 2020-2021. Analysis includes descriptive statistics, hierarchical cluster analysis, segmentation and logistic regressions.

Results

Self-reported intention to vaccinate increased after the introduction of COVID-19 vaccines in Kosovo.[1] In less than one year, vaccination intentions increased from 36% to 66% of those unvaccinated. Predictors for vaccine intentions included gender, age, trust in health authorities. Segmentation analysis identified population segments that had high vaccine nintentions but low uptake, informing messages and campaign initiatives designed to translate intentions into behavior.

Conclusion

Identifying people's perceptions and behavior is essential to support evidence-based policy making, especially during outbreak response.

Innovation

BI is an innovative focus of research in Kosovo [1] where little BI data had been collected prior, and provided a unique understanding of population views, attitudes and behaviors related to COVID-19. These findings were not only essential for an evidence-based pandemic response but also laid the foundation for future broad application of BI to inform interventions that seek to enable, support and promote health-related behaviurs in Kosovo[1].

Translation and validation of the Chinese self-report version of Spinal Cord Independence Measure (SCIM-SR): Rasch psychometric analysis and online application

Spinal Cord Independence Measure (SCIM) was an important functional outcome measure specifically designed for spinal cord injury (SCI) patients, with the self-reported version of SCIM (SCIM-SR) published in 2013. This study aims to translate the SCIM-SR into Chinese, and to investigate the validity of Chinese SCIM-SR among SCI patients. This Chinese version of SCIM-SR was translated into Chinese in a standardized approach, and then filled out by a sample of patients with SCI (n = 205) within 3 days after admission. Validity of Chinese SCIM-SR was then analyzed using Rasch analysis and principal component analysis. The subscale Selfcare and subscale Mobility showed good fit to the Rasch model, with no significance found in Chi-square test results for item-trait interaction, using Bonferroni adjustment for the significant level (χ2 =18.125, P = 0.111; χ2 =33.629, P = 0.006). Mean fit residual for items and persons of each subscale were within ± 2.5. The model fit of the subscale of Respiration and Sphincter Management was not satisfactory even after deleting one item and merging two items with local dependence. However, Kaiser-Meyer-Olkin test was > 0.50 in total score and all the subscales of Chinese SCIM-SR, and P < 0.05 in the Bartlett's test. There was no differential item functioning for gender, time post injury, age, and etiology in any of the three subscales. An online version of Chinese SCIM-SR was also developed. It is concluded that the SCIM-SR in Chinese is valid for application in individuals with SCI. SCIM-SR is considered as an important tool for self-reporting functional status from SCI individuals' perspective.

Characteristics and phylogenetic distribution of megaplasmids and prediction of a putative chromid in Pseudomonas aeruginosa

Research on megaplasmids that contribute to the spread of antimicrobial resistance (AMR) in Pseudomonas aeruginosa strains has grown in recent years due to the now widely used technologies allowing long-read sequencing. Here, we systematically analyzed distinct and consistent genetic characteristics of megaplasmids found in P. aeruginosa. Our data provide information on their phylogenetic distribution and hypotheses tracing the potential evolutionary paths of megaplasmids. Most of the megaplasmids we found belong to the IncP-2-type, with conserved and syntenic genetic backbones carrying modules of genes associated with chemotaxis apparatus, tellurite resistance and plasmid replication, segregation, and transmission. Extensively variable regions harbor abundant AMR genes, especially those encoding β-lactamases such as VIM-2, IMP-45, and KPC variants, which are high-risk elements in nosocomial infection. IncP-2 megaplasmids act as effective vehicles transmitting AMR genes to diverse regions. One evolutionary model of the origin of megaplasmids claims that chromids can develop from megaplasmids. These chromids have been characterized as an intermediate between a megaplasmid and a chromosome, also containing core genes that can be found on the chromosome but not on the megaplasmid. Using in silico prediction, we identified the "PABCH45 unnamed replicon" as a putative chromid in P. aeruginosa, which shows a much higher similarity and closer phylogenetic relationship to chromosomes than to megaplasmids while also encoding plasmid-like partition genes. We propose that such a chromid could facilitate genome expansion, allowing for more rapid adaptations to novel ecological niches or selective conditions, in comparison to megaplasmids.

COVID-19 vaccination in healthcare workers: Long-term benefits and protection

Introduction

This study aimed to evaluate the long-term effectiveness of COVID-19 vaccination in healthcare workers by analyzing the population's response to the vaccine after two years, based on anti-SARS-CoV-2 protein S antibody levels. Additionally, the study aimed to assess the impact of basic factors on antibody levels.

Material and methods

A total of 4,090 healthcare workers were included in the study, and their antibody levels were measured using ELISA to detect anti-SARS-CoV-2 immunoglobulin G (IgG). Statistical analysis was conducted to examine the influence of COVID-19 infection, vaccination status, and number of vaccine doses on antibody concentrations.

Results and Conclusion

The majority of participants (85.1%) received the Pfizer/BioNTech vaccine, while a smaller percentage chose vector vaccines such as AstraZeneca and Johnson & Johnson. The incidence of COVID-19 among vaccinated individuals was relatively low for all vaccines, confirming their effectiveness in preventing symptomatic SARS-CoV-2 infection. The study observed variations in IgG antibody levels within the study population, with only 0.46% of individuals testing negative for the presence of antibodies. The average anti-SARS-CoV-2 IgG values showed significant differences across consecutive 3-month periods following infection or vaccination, with a gradual decrease over time. Notably, the most significant changes in antibody levels were observed within the first 6 months (mean values ranged from 3647.11 BAU/ml to 2601.49 BAU/ml). Subsequently, minor fluctuations were observed, with mean antibody values hovering around 2000 BAU/ml. The differences between average anti-SARS-CoV-2 IgG values between consecutive 3-month periods from disease onset were statistically significant.

Evaluating performance on the Glittre-ADL test in men with long COVID 3 years after a SARS-CoV-2 infection

Background/objective

Many COVID-19 survivors, especially those who have been hospitalized, have been suffering numerous complications that limit their activities of daily living, although changes that persist 3 years after infection are still not known. We aimed to investigate the impact of long COVID on the Glittre-ADL test (TGlittre) 3 years after acute infection in men who needed hospitalization and explore whether the performance on the TGlittre is associated with impairments in lung function, muscle strength, physical function and quality of life (QoL).

Methods

Cross-sectional study with 42 men with long COVID who took the TGlittre. They underwent pulmonary function tests and measurements of handgrip strength and quadriceps strength (QS). Additionally, they also completed the Saint George Respiratory Questionnaire (SGRQ) and Functional Independence Measure (FIM).

Results

The mean age was 52 ± 10.6 years, while the mean time after diagnosis of COVID-19 was 37 ± 3.5 months. The mean TGlittre time was 3.3 (3.1-4.1) min, which was 10% greater than the time expected for normal individuals to complete it. The TGlittre time was correlated significantly with the QS (rs = -0.397, p = 0.009), pulmonary diffusion (rs = - 0.364, p = 0.017), FIM (rs = -0.364, p = 0.017) and the "activity" domain score of the SGRQ (rs = 0.327, p = 0.034).

Conclusion

Functional capacity on exertion as measured by the TGlittre time is normal in most men with long COVID 3 years after hospitalization. However, this improvement in functional capacity does not seem to be reflected in muscle strength or QoL, requiring continued monitoring even after 3 years.

Exploring research on ecotechnology through artificial intelligence and bibliometric maps

Ecotechnology, quintessential for crafting sustainable socio-environmental strategies, remains tantalizingly uncharted. Our analysis, steered by the nuances of machine learning and augmented by bibliometric insights, delineates the expansive terrain of this domain, elucidates pivotal research themes and conundrums, and discerns the vanguard nations in this field. Furthermore, we deftly connect our discoveries to the United Nations' 2030 Sustainable Development Goals, thereby accentuating the profound societal ramifications of ecotechnology.

Role of extracellular vesicles associated with microRNAs and their interplay with cuproptosis in osteoporosis

Osteoporosis (OP)-associated fractures can result in severe morbidity and disability, reduced quality of life, and death. Previous studies have suggested that small noncoding RNAs, for example, small regulatory microRNAs (miRNAs), play a key role in OP by inhibiting target gene expression. Cuproptosis, a recently proposed copper-induced cell death pathway, is linked with OP. Here, we describe the contribution of exosomal miRNAs and cuproptosis to OP. First, we highlight the characteristics of exosomes and roles of exosome-related miRNAs. Next, we discuss the relationship between cuproptosis and OP. Subsequently, we analyze the crosstalk of exosomal miRNAs with cuproptosis in the development of OP. This review aims to investigate a new clinical treatment method for OP.

Extracellular polymeric substances as paper coating biomaterials derived from anaerobic granular sludge

Recovering extracellular polymeric substances (EPS) from waste granular sludge offers a cost-effective and sustainable approach for transforming wastewater resources into industrially valuable products. Yet, the application potential of these EPS in real-world scenarios, particularly in paper manufacturing, remains underexplored. Here we show the feasibility of EPS-based biomaterials, derived from anaerobic granular sludges, as novel coating agents in paper production. We systematically characterised the rheological properties of various EPS-based suspensions. When applied as surface sizing agents, these EPS-based biomaterials formed a distinct, ultra-thin layer on paper, as evidenced by scanning electron microscopy. A comprehensive evaluation of water and oil penetration, along with barrier properties, revealed that EPS-enhanced coatings markedly diminished water absorption while significantly bolstering oil and grease resistance. Optimal performance was observed in EPS variants with elevated protein and hydrophobic contents, correlating with their superior rheological characteristics. The enhanced water-barrier and grease resistance of EPS-coated paper can be attributed to its non-porous, fine surface structure and the functional groups in EPS, particularly the high protein content and hydrophobic humic-like substances. This research marks the first demonstration of utilizing EPS from anaerobic granular sludge as paper-coating biomaterials, bridging a critical knowledge gap in the sustainable use of biopolymers in industrial applications.

Mir-155-5p targets TP53INP1 to promote proliferative phenotype in hypersensitivity pneumonitis lung fibroblasts

Background

Hypersensitivity pneumonitis (HP) is an inflammatory disorder affecting lung parenchyma and often evolves into fibrosis (fHP). The altered regulation of genes involved in the pathogenesis of the disease is not well comprehended, while the role of microRNAs in lung fibroblasts remains unexplored.

Methods

We used integrated bulk RNA-Seq and enrichment pathway bioinformatic analyses to identify differentially expressed (DE)-miRNAs and genes (DEGs) associated with HP lungs. In vitro, we evaluated the expression and potential role of miR-155-5p in the phenotype of fHP lung fibroblasts. Loss and gain assays were used to demonstrate the impact of miR-155-5p on fibroblast functions. In addition, mir-155-5p and its target TP53INP1 were analyzed after treatment with TGF-β, IL-4, and IL-17A.

Results

We found around 50 DEGs shared by several databases that differentiate HP from control and IPF lungs, constituting a unique HP lung transcriptional signature. Additionally, we reveal 18 DE-miRNAs that may regulate these DEGs. Among the candidates likely associated with HP pathogenesis was miR-155-5p. Our findings indicate that increased miR-155-5p in fHP fibroblasts coincides with reduced TP53INP1 expression, high proliferative capacity, and a lack of senescence markers compared to IPF fibroblasts. Induced overexpression of miR-155-5p in normal fibroblasts remarkably increases the proliferation rate and decreases TP53INP1 expression. Conversely, miR-155-5p inhibition reduces proliferation and increases senescence markers. TGF-β, IL-4, and IL-17A stimulated miR-155-5p overexpression in HP lung fibroblasts.

Conclusion

Our findings suggest a distinctive signature of 53 DEGs in HP, including CLDN18, EEF2, CXCL9, PLA2G2D, and ZNF683, as potential targets for future studies. Likewise, 18 miRNAs, including miR-155-5p, could be helpful to establish differences between these two pathologies. The overexpression of miR-155-5p and downregulation of TP53INP1 in fHP lung fibroblasts may be involved in his proliferative and profibrotic phenotype. These findings may help differentiate and characterize their pathogenic features and understand their role in the disease.

miR-218-5p, miR-124-3p and miR-23b-3p act synergistically to modulate the expression of NACC1, proliferation, and apoptosis in C-33A and CaSki cells

Background

In cervical cancer (CC), miR-218-5p, -124-3p, and -23b-3p act as tumor suppressors. These miRNAs have specific and common target genes that modulate apoptosis, proliferation, invasion, and migration; biological processes involved in cancer.

Methods

miR-218-5p, -124-3p, and -23b-3p mimics were transfected into C-33A and CaSki cells, and RT-qPCR was used to quantify the level of each miRNA and NACC1. Proliferation was assessed by BrdU and apoptosis by Annexin V/PI. In the TCGA and The Human Protein Atlas databases, the level of NACC1 mRNA and protein (putative target of the three miRNAs) was analyzed in CC and normal tissue. The relationship of NACC1 with the overall survival in CC was analyzed in GEPIA2. NACC1 mRNA and protein levels were higher in CC tissues compared with cervical tissue without injury.

Results

An increased expression of NACC1 was associated with lower overall survival in CC patients. The levels of miR-218-5p, -124-3p, and -23b-3p were lower, and NACC1 was higher in C-33A and CaSki cells compared to HaCaT cells. The increase of miR-218-5p, -124-3p, and -23b-3p induced a significant decrease in NACC1 mRNA. The transfection of the three miRNAs together caused more drastic changes in the level of NACC1, in the proliferation, and in the apoptosis with respect to the individual transfections of each miRNA.

Conclusion

The results indicate that miR-218-5p, -124-3p, and -23b-3p act synergistically to decrease NACC1 expression and proliferation while promoting apoptosis in C-33A and CaSki cells. The levels of NACC1, miR-218-5p, -124-3p, and -23b-3p may be a potential prognostic indicator in CC.

MicroRNA-mediated epigenetic regulation of HDAC8 and HDAC6: Functional significance in cervical cancer

Cervical cancer, a leading global cause of female mortality, exhibits diverse molecular aberrations influencing gene expression and signaling pathways. Epigenetic factors, including histone deacetylases (HDACs) such as HDAC8 and HDAC6, along with microRNAs (miRNAs), play pivotal roles in cervical cancer progression. Recent investigations have unveiled miRNAs as potential regulators of HDACs, offering a promising therapeutic avenue. This study employed in-silico miRNA prediction, qRT-PCR co-expression studies, and Dual-Luciferase reporter assays to identify miRNAs governing HDAC8 and HDAC6 in HeLa, cervical cancer cells. Results pinpointed miR-497-3p and miR-324-3p as novel negative regulators of HDAC8 and HDAC6, respectively. Functional assays demonstrated that miR-497-3p overexpression in HeLa cells suppressed HDAC8, leading to increased acetylation of downstream targets p53 and α-tubulin. Similarly, miR-324-3p overexpression inhibited HDAC6 mRNA and protein expression, enhancing acetylation of Hsp90 and α-tubulin. Notably, inhibiting HDAC8 via miRNA overexpression correlated with reduced cell viability, diminished epithelial-to-mesenchymal transition (EMT), and increased microtubule bundle formation in HeLa cells. In conclusion, miR-497-3p and miR-324-3p emerge as novel negative regulators of HDAC8 and HDAC6, respectively, with potential therapeutic implications. Elevated expression of these miRNAs in cervical cancer cells holds promise for inhibiting metastasis, offering a targeted approach for intervention in cervical malignancy.

A potential function for MicroRNA-124 in normal and pathological bone conditions

Cells produce short single-stranded non-coding RNAs (ncRNAs) called microRNAs (miRNAs), which actively regulate gene expression at the posttranscriptional level. Several miRNAs have been observed to exert significant impacts on bone health and bone-related disorders. One of these, miR-124, is observed in bone microenvironments and is conserved across species. It affects bone cell growth and differentiation by activating different transcription factors and signaling pathways. In-depth functional analyses of miR-124 have revealed several physiological and pathological roles exerted through interactions with other ncRNAs. Deciphering these RNA-mediated signaling networks and pathways is essential for understanding the potential impacts of dysregulated miRNA functions on bone biology. In this review, we aim to provide a comprehensive analysis of miR-124's involvement in bone physiology and pathology. We highlight the importance of miR-124 in controlling transcription factors and signaling pathways that promote bone growth. This review reveals therapeutic implications for the treatment of bone-related diseases.

Longitudinal course of core cognitive domains in first-episode acute and transient psychotic disorders compared with schizophrenia

Acute and transient psychotic disorder (ATPD) is characterized by acute onset of psychotic symptoms and early recovery. Contrastingly, schizophrenia (SZ) is a chronic mental disorder characterized by impaired functioning including a deficit in cognition. In SZ, the cognitive deficit is among the core symptoms, but in ATPDs, the existing evidence brings mixed results. Our primary aim was to compare three core cognitive domains (executive functioning/abstraction, speed of processing and working memory) of patients diagnosed with ATPD and SZ over a 12-month period. Moreover, we explored how these diagnostic subgroups differed in their clinical characteristics. We recruited 39 patients with a diagnosis of SZ and 31 with ATPD with schizophrenic symptoms. All patients completed clinical and neuropsychological assessments. At baseline, we used a one-way ANCOVA model with a group as the between-subjects factor. Mixed-model repeated-measures ANOVAs with time as the within-subjects factor and group as the between-subjects factor were run to test the overtime differences. At baseline, we did not find any differences in cognition - with sex, education and age as covariates - between ATPDs and SZ. After one year, all patients showed an improvement in all three domains, however, there were no significant overtime changes between ATPDs and SZ. Regarding clinical profiles, ATPDs demonstrated less severe psychopathology and better functioning compared to SZ both at baseline and after 12 months. The medication dosage differed at retest, but not at baseline between the groups. Our findings suggest clinical differences and a similar trajectory of cognitive performance between these diagnostic subgroups.

Dynamics design of a non-natural transcription factor responding to androst-4-ene-3,17-dione

The production of androst-4-ene-3,17-dione (AD) by the steroidal microbial cell factory requires transcription factors (TFs) to participate in metabolic regulation. However, microbial cell factory lacks effective TFs that can respond to AD in its metabolic pathway. Additionally, finding and obtaining natural TFs that specifically respond to AD is a complex and onerous task. In this study, we devised an artificial TF that responds to AD, termed AdT, based on structure-guided molecular dynamics (MD) simulation. According to MD analysis of the conformational changes of AdT after binding to AD, an LBD in which the N- and C-termini exhibited convergence tendencies was used as a microswitch to guide the assembly of a DNA-binding domain lexA, a linker (GGGGS)2, and a transcription activation domain B42 into an artificial TF. As a proof of design, a AD biosensor was designed and constructed in yeast on the basis of the ligand-binding domain (LBD) of hormone receptor. In addition, the transcription factor activity of AdT was increased by 1.44-fold for its variant F320Y. Overall, we created non-natural TF elements for AD microbial cell factory, and expected that the design TF strategy will be applied to running in parallel to the signaling machinery of the host cell.

Crop residue burning in China (2019-2021): Spatiotemporal patterns, environmental impact, and emission dynamics

Crop residue burning (CRB) is a major contributor to air pollution in China. Current fire detection methods, however, are limited by either temporal resolution or accuracy, hindering the analysis of CRB's diurnal characteristics. Here we explore the diurnal spatiotemporal patterns and environmental impacts of CRB in China from 2019 to 2021 using the recently released NSMC-Himawari-8 hourly fire product. Our analysis identifies a decreasing directionality in CRB distribution in the Northeast and a notable southward shift of the CRB center, especially in winter, averaging an annual southward movement of 7.5°. Additionally, we observe a pronounced skewed distribution in daily CRB, predominantly between 17:00 and 20:00. Notably, nighttime CRB in China for the years 2019, 2020, and 2021 accounted for 51.9%, 48.5%, and 38.0% respectively, underscoring its significant environmental impact. The study further quantifies the hourly emissions from CRB in China over this period, with total emissions of CO, PM10, and PM2.5 amounting to 12,236, 2,530, and 2,258 Gg, respectively. Our findings also reveal variable lag effects of CRB on regional air quality and pollutants across different seasons, with the strongest impacts in spring and more immediate effects in late autumn. This research provides valuable insights for the regulation and control of diurnal CRB before and after large-scale agricultural activities in China, as well as the associated haze and other pollution weather conditions it causes.