Acta Anaesthesiol Scand. 2025 Mar;69(3):e14588. doi: 10.1111/aas.14588.

ABSTRACT

BACKGROUND: Remimazolam, a novel ultra-short-acting benzodiazepine, has gained popularity in various anesthetic applications due to its pharmacokinetic advantages. However, as its use increases, safety concerns also rise, necessitating thorough examination. Additionally, the limited reports on its side effects require a broader investigation to better understand the drug's safety profile.

METHODS: This observational study systematically investigated adverse drug events (ADEs) associated with remimazolam using the FAERS database from Q1 2020 to Q4 2023. The primary objective was to assess potential safety signals and provide comprehensive information for clinical and regulatory purposes.

RESULTS: A total of 67 cases and 161 ADEs were identified. The incidence of ADEs was higher in patients aged >45 years, particularly those >65 years. Intravenous general anaesthesia was the most common administration method. Notable ADE signals included serious events such as allergic reactions, respiratory and cardiac arrest, and vascular access occlusion.

CONCLUSION: Clinicians should be vigilant about potential allergic reactions to remimazolam, especially in older patients, and avoid off-label use until more data are available. Continuous monitoring of post-market surveillance data is essential for uncovering undetected ADEs and ensuring the safe use of remimazolam.

EDITORIAL COMMENT: This study analyzed adverse drug events (ADEs) associated with remimazolam using the FAERS database, identifying serious safety signals like allergic reactions, respiratory and cardiac arrests, and vascular access site occlusions, especially in older patients. The findings highlight the need for vigilant monitoring, cautious off-label use, and ongoing post-marketing surveillance.

PMID:39948627 | DOI:10.1111/aas.14588

Sci Rep. 2025 Feb 13;15(1):5365. doi: 10.1038/s41598-025-89128-4.

ABSTRACT

Ulcerative colitis (UC), a chronic inflammatory bowel disease, significantly increases the risk of colon adenocarcinoma (COAD). Disulfidptosis, a novel form of programmed cell death, has been implicated in various diseases, including UC. This study investigates the expression of disulfidptosis-related genes, particularly CD2AP and MYH10, in UC and COAD. Through analysis of public datasets, we found MYH10 significantly upregulated and CD2AP downregulated in UC compared to healthy controls, with consistent patterns in COAD. Immune infiltration analysis revealed correlations between these genes and specific immune cell types, suggesting their roles in immune modulation. Molecular docking showed strong binding affinities of UC drugs such as budesonide and sulfasalazine with CD2AP and MYH10. Connectivity Map analysis identified additional drug candidates, including simvastatin and mephenytoin, which may be repurposed for UC and COAD therapy. These findings suggest disulfidptosis-related genes as potential biomarkers and therapeutic targets, linking chronic inflammation to cancer progression.

PMID:39948102 | DOI:10.1038/s41598-025-89128-4

Parasitol Int. 2025 Feb 11:103041. doi: 10.1016/j.parint.2025.103041. Online ahead of print.

ABSTRACT

The malarial parasite Plasmodium falciparum has evolved resistance to several antimalarial drugs, posing a significant challenge to the effective management and treatment of malaria in endemic areas. Drug repurposing has emerged as a potential alternative strategy for addressing this issue. This study aimed to identify an FDA-approved microbicidal agent, zinc pyrithione (ZPT), against mixed blood-stage parasites of P. falciparum chloroquine-sensitive (Pf3D7) and resistant strains (PfINDO). Based on the time-inhibition kinetics assay, the parasite viability was significantly inhibited by ZPT treatment for 96 (0.77 μM and 0.37 μM) and 72 h (0.63 μM and 0.61 μM), followed by 48 h (0.76 μM and 1.32 μM) and moderate inhibitory effects for 12 and 24 h in both Pf3D7 and PfINDO culture. Stage-specific treatment revealed that trophozoites and schizonts exposed to ZPT were more susceptible than ring-stage parasites. Phenotypic assays revealed that trophozoites and schizonts failed to mature and exhibited aberrant morphologies such as condensed nuclei, as determined by Giemsa staining. Furthermore, ZPT in combination with dihydroartemisinin and chloroquine demonstrated additive interactions in both Pf3D7 and PfINDO parasites. At therapeutic dosages, ZPT failed to cause hemolysis in human erythrocytes. Overall, this study demonstrated a time-dependent effect of ZPT on the blood stages of human P. falciparum in culture, suggesting its utility in clinical settings.

PMID:39947389 | DOI:10.1016/j.parint.2025.103041

Int J Pharm. 2025 Feb 11:125342. doi: 10.1016/j.ijpharm.2025.125342. Online ahead of print.

ABSTRACT

Adrenal insufficiency, an orphan disease, may lead to significant morbidity despite its rare occurrence. Therefore, it requires a daily replacement therapy of hydrocortisone, which displays a highly variable pharmacokinetic profile in individual patients, highlighting the need for personalized dosing. Like most hormones, cortisol follows a circadian rhythm and most conventional dosage forms fail to result in an accurate chronorelease profile. Semi-solid extrusion 3D printing can design unique dosage forms that have the potential to address such needs. Despite several developments and investigations in this area, the existing formulations either fail to facilitate the nocturnal release of cortisol or are unable to meet the personal requirements of patients. Our investigation, thus, focuses on a tablet-in-tablet (core-shell tablet) approach to enable nocturnal release of hydrocortisone and provide personalized dosing. The shell consisted of Klucel™ HF, which acted as rate limiting barrier and provided an initial delayed release of the drug whereas the core comprised of the drug, along with soluble and insoluble diluents, suspended in Klucel™ JF gel. The resulting paste was characterized by its rheology. The optimum parameters for printing both, the core and shell paste were found to be nozzle gauge of 21G, printing speed of 15 mm/s, and the layer height of 0.51 mm. Physicochemical characterization of tablets was conducted with respect to measuring their breaking force, friability, drug content, FTIR, X-ray powder diffraction, SEM, and in-vitro dissolution. This work successfully demonstrates the potential of SSE 3D printing to fabricate Chronotherpeutic release personalized Tablets to improve patient compliance and treatment adherence.

PMID:39947361 | DOI:10.1016/j.ijpharm.2025.125342

BMC Genomics. 2025 Feb 13;26(1):140. doi: 10.1186/s12864-025-11310-9.

ABSTRACT

BACKGROUND: The cytochrome P450 family 2 subfamily C member 9 (CYP2C9) exhibits extensive genetic variability that may influence the metabolism of approximately 16-20% of all drugs. Understanding the frequency and functional impact of the CYP2C9 variants is crucial for the implementation of pharmacogenetics. Our study aims to determine the frequencies of CYP2C9 variants in the Syrian population, contributing to the limited information available for Middle Eastern populations.

METHODS: One hundred thirty-eight unrelated individuals from two major Syrian cities (Damascus and Homs) enrolled in this cross-sectional study. Genomic DNA was extracted from peripheral blood and specific PCR amplification products were purified and sequenced. The length of the amplicons allowed for the detection of 17 star alleles (i.e. *2, *8, *14, *20, *26, *33, *40, *41, *42, *43, *45, *46, *62, *63, *72, *73, and *78) in exon three, and seven star alleles (i.e., *3, *4, *5, *24, *55, *66, *68) in exon seven, in addition to two intronic variants. The frequencies of the functionally compromised CYP2C9*2rs1799853 and CYP2C9*3rs1057910 alleles were compared to same variants in other populations.

RESULTS: Of the 24 exonic alleles investigated, only the *2, *3, *41, and *46 alleles were detected at frequencies of 14.8%, 8.3%, 1.45%, and 0.72%, respectively, with 43.5% of the study subjects carrying at least one dysfunctional variant. The genotype frequencies observed were as follows: *1/*1 (56.5%), *1/*2 (23.9%), *2/*2 (0.7%), *3/*1 (12.3%), *2/*3 (4.3%), *3/*3 (0%), *1/*41 (0.7%), *2/*41 (0%), *3/*41 (0.7%), *1/*46 (0.7%), *46/*2 (0%), and *46/*3 (0%). Moreover, frequencies of the rs933120 and rs933119 intronic alleles were 12.3% and 6.1%, respectively. A high linkage disequilibrium (LD) was found (D'=0.78) between the intronic rs933119 and exonic rs1799853 (*2 allele).

CONCLUSIONS: This study provides evidence for high prevalence of the CYP2C9 *2 and *3 alleles, and consequently the intermediate and poor metabolizer phenotypes in Syrians. Two rare putative function-relevant variants (*41 and *46) were detected in three individuals. These findings pave the path to the efforts for implementing CYP2C9 pharmacogenetics-based personalized pharmacotherapy in this Middle Eastern population.

PMID:39948503 | DOI:10.1186/s12864-025-11310-9

Pulm Ther. 2025 Feb 13. doi: 10.1007/s41030-025-00287-1. Online ahead of print.

ABSTRACT

INTRODUCTION: Elexacaftor/tezacaftor/ivacaftor (ETI) has been shown to substantially improve clinical outcomes among people living with cystic fibrosis (pwCF). The impact of ETI on health care resource utilization in the context of universal health care is largely unknown. We aimed to assess the impact of ETI on hospital and non-hospital health care resource utilization in a national cohort of pwCF up to 2 years after ETI initiation.

METHODS: We included all pwCF aged 12 years or older in the Danish Cystic Fibrosis Cohort initiating ETI therapy between 1 September 2020 and 31 December 2022. The following health care contacts were reported: acute and elective hospitalizations, acute and elective outpatient contacts, general practitioner (GP) visits, other specialist visits, physiotherapist/chiropractor visits, pharmacy visits, and blood sampling appointments. Pre- and post-ETI data were analyzed using logistic and linear regression models estimating number of visits, days in hospital, and odds ratios (ORs) for one monthly contact.

RESULTS: A total of 283 pwCF initiated ETI in the study period. At 24 months post-ETI, utilization of the following health care resources was reduced: elective hospitalizations [OR 0.20 (95% CI: 0.08; 0.50)], elective outpatient hospital contacts [0.70 (0.57; 0.86)], pharmacy visits [0.56 (0.45; 0.71)], and blood sampling appointments [0.61 (0.49; 0.77)]. Number of contacts per month was reduced for the aforementioned outcomes, as well as number of days in hospital for elective hospitalizations. A downward but not statistically significant trend was observed for acute hospitalizations. No significant change was observed for acute outpatient visits, GP visits, other specialist visits, or visits to a physiotherapist/chiropractor.

CONCLUSION: In a national cohort of pwCF, ETI was associated with substantial reductions in elective hospitalizations, elective outpatient contacts, duration of elective hospitalizations, pharmacy visits, and blood sampling appointments, sustained 2 years post-ETI initiation. These findings highlight the real-world effectiveness of ETI in the context of a universal health care system.

PMID:39948204 | DOI:10.1007/s41030-025-00287-1

J Cyst Fibros. 2025 Feb 12:S1569-1993(25)00049-9. doi: 10.1016/j.jcf.2025.01.016. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic pulmonary inflammation strongly contributes to respiratory failure and mortality in patients with cystic fibrosis (pwCF). Effective anti-microbial immunity and maintaining lung homeostasis require continuous structural-immune cell communication. Whether and how this crosstalk is altered in CF remains poorly understood, obscuring potential new angles for therapy development to restore airway homeostasis in pwCF.

METHODS: We performed droplet-based single cell RNA-sequencing on bronchial biopsies from pwCF to investigate structural-immune cell crosstalk. Computational analyses were used to compare these data to samples obtained from healthy controls.

RESULTS: CF airway wall biopsies showed lower proportions and altered transcriptomes of basal cells, submucosal gland cells and endothelial cells, and a higher abundance of ciliated cells, monocytes, macrophages and T cells. Both B and T lymphocytes displayed aberrantly activated phenotypes with transcriptional changes linked to hypoxia and vascular endothelial growth factor signaling, indicative of crosstalk with endothelial cells. The CF lung displayed unique changes in intercellular communication potential involving ionocytes, macrophages, endothelial cells and lymphocytes. This included interactions between HLA-E on structural cells and the druggable CD94/NKG2A immune checkpoint on CD8+T cells.

CONCLUSIONS: We report the first single cell transcriptome atlas of the CF lung containing the full spectrum of structural and immune cells, providing a valuable resource for investigating changes to cellular composition, phenotypes and crosstalk linked to CF. Our analyses highlight dysregulated basal cell function and adaptive immunity in pwCF - despite favorable responses to CFTR modulator therapy. We identify novel aspects of CF pathophysiology and potential entry points for therapeutic strategies.

PMID:39947933 | DOI:10.1016/j.jcf.2025.01.016

J Cyst Fibros. 2025 Feb 12:S1569-1993(25)00050-5. doi: 10.1016/j.jcf.2025.01.017. Online ahead of print.

NO ABSTRACT

PMID:39947932 | DOI:10.1016/j.jcf.2025.01.017

Int J Epidemiol. 2024 Dec 16;54(1):dyaf006. doi: 10.1093/ije/dyaf006.

ABSTRACT

BACKGROUND: During the COVID-19 pandemic, mortality from some chronic diseases increased. In this study, we evaluated monthly excess mortality from all causes, cancer, cardiovascular diseases (CVD) and diabetes during the months of 2020 and 2021, examining its relationship with COVID-19 cases.

METHODS: Monthly cause-specific mortality data were downloaded from public repositories of national statistics offices or directly requested from them, and population data were obtained from the United Nations archives. Excess deaths were estimated as the difference between observed and expected deaths. Monthly expected deaths for 2020 and 2021 were calculated using a quasi-Poisson regression model trained on 2010-19 data (or a shorter timespan if the full decade of data was not available). To quantify the correlation between COVID-19 cases and monthly excess mortality, we used the Spearman's correlation coefficient (rs).

RESULTS: The study included 16 countries that provided monthly national data on causes of death (Argentina, Austria, Brazil, Switzerland, Chile, the Czech Republic, Germany, Georgia, Hungary, Italy, Lithuania, Latvia, Mexico, Serbia, Slovakia and the USA). A positive correlation was found between COVID-19 cases and monthly excess mortality from all causes in all countries (rs ranging from 0.61 to 0.91), from CVD in 11 countries (rs ranging from 0.45 to 0.85) and for diabetes in 13 countries (rs ranging from 0.42 to 0.79). Excess mortality above 5% was estimated from all causes in 14 countries for both 2020 and 2021, from CVD in seven countries for 2020 and in nine countries for 2021, and from diabetes in 11 countries for 2020 and in 12 countries for 2021. No excess above 5% was estimated for cancer mortality in any of the countries considered.

CONCLUSIONS: Excess mortality from CVD and diabetes persisted in several countries throughout 2021. These increases coincide with COVID-19 peaks, supporting a short-term impact of the COVID-19 pandemic on mortality from these causes.

PMID:39947655 | DOI:10.1093/ije/dyaf006

PLoS Pathog. 2025 Feb 13;21(2):e1012922. doi: 10.1371/journal.ppat.1012922. eCollection 2025 Feb.

ABSTRACT

Persistent bacterial infections evade host immunity and resist antibiotic treatments through various mechanisms that are difficult to evaluate in a living host. Pseudomonas aeruginosa is a main cause of chronic infections in patients with cystic fibrosis (CF) and wounds. Here, by immersing wounded zebrafish embryos in a suspension of P. aeruginosa isolates from CF patients, we established a model of persistent infection that mimics a murine chronic skin infection model. Live and electron microscopy revealed persisting aggregated P. aeruginosa inside zebrafish cells, including macrophages, at unprecedented resolution. Persistent P. aeruginosa exhibited adaptive resistance to several antibiotics, host cell permeable drugs being the most efficient. Moreover, persistent bacteria could be partly re-sensitized to antibiotics upon addition of anti-biofilm molecules that dispersed the bacterial aggregates in vivo. Collectively, this study demonstrates that an intracellular location protects persistent P. aeruginosa in vivo in wounded zebrafish embryos from host innate immunity and antibiotics, and provides new insights into efficient treatments against chronic infections.

PMID:39946497 | DOI:10.1371/journal.ppat.1012922

NMR Biomed. 2025 Mar;38(3):e70001. doi: 10.1002/nbm.70001.

ABSTRACT

Due to the complex structure of the brain, variations in tumor shapes and sizes, and the resemblance between tumor and healthy tissues, the reliable and efficient identification of brain tumors through magnetic resonance imaging (MRI) presents a persistent challenge. Given that manual identification of tumors is often time-consuming and prone to errors, there is a clear need for advanced automated procedures to enhance detection accuracy and efficiency. Our study addresses the difficulty by creating an improved convolutional neural network (CNN) framework derived from DenseNet121 to augment the accuracy of brain tumor detection. The proposed model was comprehensively evaluated against 12 baseline CNN models and 5 state-of-the-art architectures, namely Vision Transformer (ViT), ConvNeXt, MobileNetV3, FastViT, and InternImage. The proposed model achieved exceptional accuracy rates of 98.4% and 99.3% on two separate datasets, outperforming all 17 models evaluated. Our improved model was integrated using Explainable AI (XAI) techniques, particularly Grad-CAM++, facilitating accurate diagnosis and localization of complex tumor instances, including small metastatic lesions and nonenhancing low-grade gliomas. The XAI framework distinctly highlights essential areas signifying tumor presence, hence enhancing the model's accuracy and interpretability. The results highlight the potential of our method as a reliable diagnostic instrument for healthcare practitioners' ability to comprehend and confirm artificial intelligence (AI)-driven predictions but also bring transparency to the model's decision-making process, ultimately improving patient outcomes. This advancement signifies a significant progression in the use of AI in neuro-oncology, enhancing diagnostic interpretability and precision.

PMID:39948696 | DOI:10.1002/nbm.70001

Bioelectron Med. 2025 Feb 14;11(1):4. doi: 10.1186/s42234-025-00166-9.

ABSTRACT

The octopus has many features that make it advantageous for revealing principles of motor circuits and control and predicting behavior. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100 ms after stimulation were predictive of the resultant movement response. Machine learning models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. For kinematic analysis, deep learning models and unsupervised dimensionality reduction identified a consistent set of features that could be used to distinguish different types of arm movements. The neural circuits and the computational models identified here generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit. This study demonstrates how real-time motor behaviors can be predicted and distinguished, contributing to the development of brain-machine interfaces. The ability to accurately model and predict complex movement patterns has broad implications for advancing technologies in robotics, neuroprosthetics, and artificial intelligence, paving the way for more sophisticated and adaptable systems.

PMID:39948616 | DOI:10.1186/s42234-025-00166-9

Alzheimers Res Ther. 2025 Feb 13;17(1):41. doi: 10.1186/s13195-025-01686-x.

ABSTRACT

BACKGROUND: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treatment modes. We aimed to develop a deep learning model to predict cognitive conversion to guide re-assignment decisions to more intensive therapies where needed.

METHODS: Longitudinal data including five variable sets, i.e. demographics, medical history, neuropsychological outcomes, laboratory and neuroimaging results, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were analyzed. We first developed a deep learning model to predicted cognitive conversion using all five variable sets. We then gradually removed variable sets to obtained parsimonious models for four different years of forecasting after baseline within acceptable frames of reduction in overall model fit (AUC remaining > 0.8).

RESULTS: A total of 607 individuals were included at baseline, of whom 538 participants were followed up at 12 months, 482 at 24 months, 268 at 36 months and 280 at 48 months. Predictive performance was excellent with AUCs ranging from 0.87 to 0.92 when all variable sets were considered. Parsimonious prediction models that still had a good performance with AUC 0.80-0.84 were established, each only including two variable sets. Neuropsychological outcomes were included in all parsimonious models. In addition, biomarker was included at year 1 and year 2, imaging data at year 3 and demographics at year 4. Under our pre-set threshold, the rate of upgrade to more intensive therapies according to predicted cognitive conversion was always higher than according to actual cognitive conversion so as to decrease the false positive rate, indicating the proportion of patients who would have missed upgraded treatment based on prognostic models although they actually needed it.

CONCLUSIONS: Neurophysiological tests combined with other indicator sets that vary along the AD continuum can improve can provide aid for clinical treatment decisions leading to improved management of the disease.

TRAIL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier: NCT00106899 (Registration Date: 31 March 2005).

PMID:39948600 | DOI:10.1186/s13195-025-01686-x

BMC Cancer. 2025 Feb 13;25(1):250. doi: 10.1186/s12885-025-13622-1.

ABSTRACT

BACKGROUND: Androgen deprivation therapy (ADT) improves outcomes in men undergoing definitive radiotherapy for prostate cancer but carries significant toxicities. Clinical parameters alone are insufficient to accurately identify patients who will derive the most benefit, highlighting the need for improved patient selection tools to minimize unnecessary exposure to ADT's side effects while ensuring optimal oncological outcomes. The ArteraAI Prostate Test, incorporating a multimodal artificial intelligence (MMAI)-driven digital histopathology-based biomarker, offers prognostic and predictive information to aid in this selection. However, its clinical utility in real-world settings has yet to be measured prospectively.

METHODS: This multicentre implementation trial aims to collect real-world data on the use of the previously validated Artera MMAI-driven prognostic and predictive biomarkers in men with intermediate-risk prostate cancer undergoing curative radiotherapy. The prognostic biomarker estimates the 10-year risk of metastasis, while the predictive biomarker determines the likely benefit from short-term ADT (ST-ADT). A total of 800 participants considering ST-ADT in conjunction with curative radiotherapy will be recruited from multiple Australian centers. Eligible patients with intermediate-risk prostate cancer, as defined by the National Comprehensive Cancer Network, will be asked to participate. The primary endpoint is the percentage of patients for whom testing led to a change in the shared ST-ADT recommendation, analyzed using descriptive statistics and McNemar's test comparing recommendations before and after biomarker testing. Secondary endpoints include the impact on quality of life and 5-year disease control, assessed through linkage with the Prostate Cancer Outcomes Registry. The sample size will be re-evaluated at an interim analysis after 200 patients.

DISCUSSION: ASTuTE will determine the impact of a novel prognostic and predictive biomarker on shared decision-making in the short term, and both quality of life and disease control in the medium term. If the biomarker demonstrates a significant impact on treatment decisions, it could lead to more personalized treatment strategies for men with intermediate-risk prostate cancer, potentially reducing overtreatment and improving quality of life. A potential limitation is the variability in clinical practice across different centers inherent in real-world studies.

TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12623000713695p. Registered 5 July 2023.

PMID:39948585 | DOI:10.1186/s12885-025-13622-1

Plant Methods. 2025 Feb 13;21(1):18. doi: 10.1186/s13007-025-01332-5.

ABSTRACT

The problem of plant diseases is huge as it affects the crop quality and leads to reduced crop production. Crop-Convolutional neural network (CNN) depiction is that several scholars have used the approaches of machine learning (ML) and deep learning (DL) techniques and have configured their models to specific crops to diagnose plant diseases. In this logic, it is unjustifiable to apply crop-specific models as farmers are resource-poor and possess a low digital literacy level. This study presents a Slender-CNN model of plant disease detection in corn (C), rice (R) and wheat (W) crops. The designed architecture incorporates parallel convolution layers of different dimensions in order to localize the lesions with multiple scales accurately. The experimentation results show that the designed network achieves the accuracy of 88.54% as well as overcomes several benchmark CNN models: VGG19, EfficientNetb6, ResNeXt, DenseNet201, AlexNet, YOLOv5 and MobileNetV3. In addition, the validated model demonstrates its effectiveness as a multi-purpose device by correctly categorizing the healthy and the infected class of individual types of crops, providing 99.81%, 87.11%, and 98.45% accuracy for CRW crops, respectively. Furthermore, considering the best performance values achieved and compactness of the proposed model, it can be employed for on-farm agricultural diseased crops identification finding applications even in resource-limited settings.

PMID:39948565 | DOI:10.1186/s13007-025-01332-5

BMC Med Inform Decis Mak. 2025 Feb 13;25(1):77. doi: 10.1186/s12911-025-02870-7.

ABSTRACT

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) represents a significant global health challenge, placing considerable burdens on healthcare systems. The rise of digital health technologies (DHTs) and artificial intelligence (AI) algorithms offers new opportunities to improve COPD predictive capabilities, diagnostic accuracy, and patient management. This systematic review explores the types of data in COPD under DHTs, the AI algorithms employed for data analysis, and identifies key application areas reported in the literature.

METHODS: A systematic search was conducted in PubMed and Web of Science for studies published up to December 2024 that applied AI algorithms in digital health for COPD management. Inclusion criteria focused on original research utilizing AI algorithms and digital health technologies for COPD, while review articles were excluded. Two independent reviewers screened the studies, resolving discrepancies through consensus.

RESULTS: From an initial pool of 265 studies, 41 met the inclusion criteria. Analysis of these studies highlighted a diverse range of data types and modalities collected from DHTs in the COPD context, including clinical data, patient-reported outcomes, and environmental/lifestyle data. Machine learning (ML) algorithms were employed in 34 studies, and deep learning (DL) algorithms in 16. Support vector machines and boosting were the most frequently used ML models, while deep neural networks (DNN) and convolutional neural networks (CNN) were the most commonly used DL models. The review identified three key application domains for AI in COPD: screening and diagnosis (10 studies), exacerbation prediction (22 studies), and patient monitoring (9 studies). Disease progression prediction was a prevalent focus across three domains, with promising accuracy and performance metrics reported.

CONCLUSIONS: Digital health technologies and AI algorithms have a wide range of applications and promise for COPD management. ML models, in particularly, show great potential in improving digital health solutions for COPD. Future research should focus on enhancing global collaboration to explore the cost-effectiveness and data-sharing capabilities of DHTs, enhancing the interpretability of AI models, and validating these algorithms through clinical trials to facilitate their safe integration into the routine COPD management.

PMID:39948530 | DOI:10.1186/s12911-025-02870-7

Commun Biol. 2025 Feb 13;8(1):233. doi: 10.1038/s42003-025-07692-x.

ABSTRACT

The rapid advancement of single-cell technologies has created an urgent need for effective methods to integrate and harmonize single-cell data. Technical and biological variations across studies complicate data integration, while conventional tools often struggle with reliance on gene expression distribution assumptions and over-correction. Here, we present scCobra, a deep generative neural network designed to overcome these challenges through contrastive learning with domain adaptation. scCobra effectively mitigates batch effects, minimizes over-correction, and ensures biologically meaningful data integration without assuming specific gene expression distributions. It enables online label transfer across datasets with batch effects, allowing continuous integration of new data without retraining. Additionally, scCobra supports batch effect simulation, advanced multi-omic integration, and scalable processing of large datasets. By integrating and harmonizing datasets from similar studies, scCobra expands the available data for investigating specific biological problems, improving cross-study comparability, and revealing insights that may be obscured in isolated datasets.

PMID:39948393 | DOI:10.1038/s42003-025-07692-x

Nat Commun. 2025 Feb 13;16(1):1577. doi: 10.1038/s41467-025-56560-z.

ABSTRACT

Mapping cellular organization in the developing brain presents significant challenges due to the multidimensional nature of the data, characterized by complex spatial patterns that are difficult to interpret without high-throughput tools. Here, we present DeepCellMap, a deep-learning-assisted tool that integrates multi-scale image processing with advanced spatial and clustering statistics. This pipeline is designed to map microglial organization during normal and pathological brain development and has the potential to be adapted to any cell type. Using DeepCellMap, we capture the morphological diversity of microglia, identify strong coupling between proliferative and phagocytic phenotypes, and show that distinct spatial clusters rarely overlap as human brain development progresses. Additionally, we uncover an association between microglia and blood vessels in fetal brains exposed to maternal SARS-CoV-2. These findings offer insights into whether various microglial phenotypes form networks in the developing brain to occupy space, and in conditions involving haemorrhages, whether microglia respond to, or influence changes in blood vessel integrity. DeepCellMap is available as an open-source software and is a powerful tool for extracting spatial statistics and analyzing cellular organization in large tissue sections, accommodating various imaging modalities. This platform opens new avenues for studying brain development and related pathologies.

PMID:39948387 | DOI:10.1038/s41467-025-56560-z

Sci Rep. 2025 Feb 13;15(1):5408. doi: 10.1038/s41598-025-89823-2.

ABSTRACT

Hundreds of loci have been robustly associated with obesity-related traits, but functional characterization of candidate genes remains a bottleneck. Aiming to systematically characterize candidate genes for a role in accumulation of lipids in adipocytes and other cardiometabolic traits, we developed a pipeline using CRISPR/Cas9, non-invasive, semi-automated fluorescence imaging and deep learning-based image analysis in live zebrafish larvae. Results from a dietary intervention show that 5 days of overfeeding is sufficient to increase the odds of lipid accumulation in adipocytes by 10 days post-fertilization (dpf, n = 275). However, subsequent experiments show that across 12 to 16 established obesity genes, 10 dpf is too early to detect an effect of CRISPR/Cas9-induced mutations on lipid accumulation in adipocytes (n = 1014), and effects on food intake at 8 dpf (n = 1127) are inconsistent with earlier results from mammals. Despite this, we observe effects of CRISPR/Cas9-induced mutations on ectopic accumulation of lipids in the vasculature (sh2b1 and sim1b) and liver (bdnf); as well as on body size (pcsk1, pomca, irs1); whole-body LDLc and/or total cholesterol content (irs2b and sh2b1); and pancreatic beta cell traits and/or glucose content (pcsk1, pomca, and sim1a). Taken together, our results illustrate that CRISPR/Cas9- and image-based experiments in zebrafish larvae can highlight direct effects of obesity genes on cardiometabolic traits, unconfounded by their - not yet apparent - effect on excess adiposity.

PMID:39948378 | DOI:10.1038/s41598-025-89823-2

Sci Rep. 2025 Feb 13;15(1):5333. doi: 10.1038/s41598-024-83084-1.

ABSTRACT

Mining-induced subsidence is one of the major concerns of mining industry/mine owners, statutory bodies, and environmental organisations. Therefore, mine subsidence monitoring and prediction is of utmost importance for its effective management. In the present study, a modified LSTM model is developed to predict the InSAR deformation time series. The modified LSTM model may also be extended for prediction based on time-series data in general. Further, to check the developed model's performance, InSAR deformation time-series results obtained from 26 TSX/TDX datasets of Mine-A in Khetri Copper Belt, India, are used as an input. Further obtained results from mLSTM have been compared with the other two models, namely RNN and LSTM. Efficiency comparison results reveal that RNN, LSTM, and modified LSTM over the applied single reference PSI-derived deformation time-series result are 82.6%, 97.54%, and 98.57%, respectively. It also reveals that the RMS error of RNN, LSTM, and modified LSTM over the applied single reference PSI-derived deformation time-series result are 6.58 mm/year, 5.34 mm/year and 4.22 mm/year, respectively. In addition, the study reveals that the prediction of the mLSTM model, compared to RNN and LSTM, is quite close to the observed/measured deformation velocity values obtained from a single reference PSI-derived result. Furthermore, prediction for the next five years using mLSTM shows that the maximum value of the deformation is -20.87 mm/year and a minimum of 4.99 mm/year. Predictions for the next five years show that most of the area is stable, but points around the plant area have shown some deformation.

PMID:39948371 | DOI:10.1038/s41598-024-83084-1