BMC Microbiol. 2025 May 21;25(1):310. doi: 10.1186/s12866-025-04024-1.

ABSTRACT

BACKGROUND: Slum dwellers face significant infrastructure and public health challenges like poor housing and drainage, inadequate sanitation, and limited access to clean water, leading to increased disease transmission and resistance to antibiotic treatments. This study evaluated the impact of heavy metals on antibiotic resistance patterns of Escherichia coli in wastewater from slums of Bwaise II, Bwaise III, Kazo, and Makerere III in Kawempe division, Kampala.

METHODS: Levels of heavy metals (lead, mercury, cadmium, chromium, and arsenic) in wastewater were determined using inductively coupled plasma mass spectroscopy. Escherichia coli were isolated from wastewater using MacConkey agar and their susceptibility to 50 µl of stock antibiotics (tetracycline, amoxicillin, ceftriaxone at 30 µg/ml, and ciprofloxacin at 5 µg/ml) determined. The potential of heavy metals to induce antibiotic resistance in Escherichia coli was determined by culturing susceptible isolates in 200 µl of Luria-Bertina broth containing stock antibiotics (10 µl), or stock antibiotics (10 µl) and stock heavy metals (10 µl). Stock heavy metals were prepared from the average concentration of heavy metals detected in wastewater.

RESULTS: Detectable levels of heavy metals were reported in wastewater from Bwaise II, Kazo and Makerere III only. Lead, cadmium and arsenic, mercury and chromium, were highest in Bwaise II, Kazo, and Makerere III, respectively. The occurrence of Escherichia coli resistant to at least an antibiotic was 72.8% (169 of 232) and resistance to tetracycline, ceftriaxone, amoxicillin, and ciprofloxacin were 34.1%, 28.9%, 35.3%, and 34.5%, respectively. Study findings further revealed a positive correlation (R2 = 0.371-0.985) between the presence of heavy metals in wastewater and antibiotic resistance patterns of Escherichia coli. Also, heavy metals; lead (77.41 µg/ml), mercury (1.44 µg/ml), and cadmium (10.21 µg/ml) significantly (p < 0.05) induced antibiotic resistance in susceptible Escherichia coli.

CONCLUSION: Wastewater in Kawempe slums is polluted with heavy metals and high prevalence of antibiotic-resistant Escherichia coli. Inadequate infrastructure in slums facilitate discharge of wastewater polluted with heavy metals, which in turn play a role in increasing antibiotic resistance. There is need for proper wastewater management to contain the prevalence of antibiotic resistance.

PMID:40399779 | DOI:10.1186/s12866-025-04024-1

Nat Biotechnol. 2025 May 21. doi: 10.1038/s41587-025-02649-1. Online ahead of print.

ABSTRACT

Cell-tagging strategies with DNA barcodes have enabled the analysis of clone size dynamics and clone-restricted transcriptomic landscapes in heterogeneous populations. However, isolating a target clone that displays a specific phenotype from a complex population remains challenging. Here we present a multi-kingdom genetic barcoding system, CloneSelect, which enables a target cell clone to be triggered to express a reporter gene for isolation through barcode-specific CRISPR base editing. In CloneSelect, cells are first stably tagged with DNA barcodes and propagated so that their subpopulation can be subjected to a given experiment. A clone that shows a phenotype or genotype of interest at a given time can then be isolated from the initial or subsequent cell pools stored during the experiment using CRISPR base editing. CloneSelect is scalable and compatible with single-cell RNA sequencing. We demonstrate the versatility of CloneSelect in human embryonic kidney 293T cells, mouse embryonic stem cells, human pluripotent stem cells, yeast cells and bacterial cells.

PMID:40399693 | DOI:10.1038/s41587-025-02649-1

Nature. 2025 May 21. doi: 10.1038/s41586-025-09041-8. Online ahead of print.

ABSTRACT

Current approaches used to track stem cell clones through differentiation require genetic engineering1,2 or rely on sparse somatic DNA variants3,4, which limits their wide application. Here we discover that DNA methylation of a subset of CpG sites reflects cellular differentiation, whereas another subset undergoes stochastic epimutations and can serve as digital barcodes of clonal identity. We demonstrate that targeted single-cell profiling of DNA methylation5 at single-CpG resolution can accurately extract both layers of information. To that end, we develop EPI-Clone, a method for transgene-free lineage tracing at scale. Applied to mouse and human haematopoiesis, we capture hundreds of clonal differentiation trajectories across tens of individuals and 230,358 single cells. In mouse ageing, we demonstrate that myeloid bias and low output of old haematopoietic stem cells6 are restricted to a small number of expanded clones, whereas many functionally young-like clones persist in old age. In human ageing, clones with and without known driver mutations of clonal haematopoieis7 are part of a spectrum of age-related clonal expansions that display similar lineage biases. EPI-Clone enables accurate and transgene-free single-cell lineage tracing on hematopoietic cell state landscapes at scale.

PMID:40399669 | DOI:10.1038/s41586-025-09041-8

Nat Commun. 2025 May 21;16(1):4720. doi: 10.1038/s41467-025-59656-8.

ABSTRACT

Ribosome biogenesis follows a cascade of pre-rRNA folding and processing steps, coordinated with ribosomal protein incorporation. Nucleolar 90S pre-ribosomes are well-described stable intermediates, composed of pre-18S rRNA, ribosomal S-proteins, U3 snoRNA, and ~70 assembly factors. However, how numerous snoRNAs control pre-rRNA modification and folding during early maturation events remains unclear. We identify snR30 (human U17), the only essential H/ACA snoRNA in yeast, which binds with Cbf5-Gar1-Nop10-Nhp2 to a pre-18S rRNA subdomain containing platform helices and ES6 of the 40S central domain. Integration into the 90S is blocked by RNA hybridization with snR30. The snoRNP complex coordinates the recruitment of early assembly factors Krr1-Utp23-Kri1 and ribosomal proteins uS11-uS15, enabling isolated subdomain assembly. Krr1-dependent release of snR30 culminates in integration of the platform into the 90S. Our study reveals the essential role of snR30 in chaperoning central domain formation as a discrete assembly unit externalized from the pre-ribosomal core.

PMID:40399280 | DOI:10.1038/s41467-025-59656-8

Neuroscience. 2025 May 19:S0306-4522(25)00393-8. doi: 10.1016/j.neuroscience.2025.05.031. Online ahead of print.

ABSTRACT

The sense of smell is essential for human perception. Olfactory function declines with increasing age, affecting a substantial portion of the elderly population, and this decline is more pronounced in men. This reduction can be attributed to anatomical and degenerative changes in the brain and olfactory receptors. There is robust clinical evidence indicating an association between olfactory perception decline/deficit (OPD) and major neurodegenerative diseases, with severe deficits observed in Alzheimer's and Parkinson's disease and milder effects noted in other conditions. However, its molecular bases have not yet been identified. Here, we explored the molecular connection between OPD and Parkinson's disease by conducting data-mining, gene enrichment analysis, and examining protein-interaction networks using systems biology approaches. We found pathways associated with both OPD and Parkinson's disease, identifying over 300 relevant genes. These genes belong to biologically relevant gene families, including transporters, kinases, nuclear receptors, transcription factors, and olfactory and other G protein-coupled receptors. Functional enrichment analysis revealed shared biological processes between OPD and Parkinson's disease, such as synaptic signalling and neuroinflammation. Mitochondrial gene enrichment was unique to Parkinson's. Both conditions exhibited a scarcity of associated genes on the Y chromosome but an even distribution on the non-pseudoautosomal region of the X chromosome, potentially explaining gender prevalence differences. In conclusion, our study suggests olfactory testing may help diagnose cognitive decline in neurodegenerative diseases. Further research is needed to understand the connection between OPD, aging, and other diseases and to examine olfactory performance in screening individuals at risk of Parkinson's disease and similar conditions.

PMID:40398724 | DOI:10.1016/j.neuroscience.2025.05.031

Int J Biochem Cell Biol. 2025 May 19:106790. doi: 10.1016/j.biocel.2025.106790. Online ahead of print.

ABSTRACT

In eukaryotes, protein arginine methylation is a prevalent post-translational modification found in a multitude of proteins responsible for key biological processes, ranging from transcription to signaling. One model suggests that phosphorylation of serine 9 (S9) in the Saccharomyces cerevisiae major protein arginine methyltransferase Hmt1 is critical for its oligomerization and activity. In this study, we used classic biochemical approaches to demonstrate that neither the S9 phosphomimetic nor the non-phosphorylatable substitution mutants of Hmt1 affect its oligomerization. These mutants remain active in vivo, retaining their ability to methylate the SR-/hnRNP-like protein Npl3 and displaying a monomethylarginine and asymmetric dimethylarginine banding profile similar to that of the wild-type. In cells lacking Dbf2, the proposed kinase responsible for phosphorylating Hmt1 at S9, Npl3 remains methylated. Additionally, monomethylarginine and asymmetric dimethylarginine banding profiles in cells lacking Dbf2 mostly resemble those observed in the wild-type rather than in hmt1Δ cells. Synchronized yeast cells expressing either S9 substitution exhibit entry into the M phase of the cell cycle at a rate similar to that of both wild-type and hmt1Δ cells. Our results suggest that the C-terminal epitope tagging of Hmt1 is responsible for the previously observed loss of enzymatic activities, rather than the S9 phosphorylation status of Hmt1. Finally, we demonstrate that S9 phosphorylation plays a role in maintaining Hmt1 protein levels in vivo. Overall, our finding demonstrates a novel role for Hmt1 S9 phosphorylation in tuning its in vivo protein levels.

PMID:40398714 | DOI:10.1016/j.biocel.2025.106790

Metab Eng. 2025 May 19:S1096-7176(25)00083-7. doi: 10.1016/j.ymben.2025.05.007. Online ahead of print.

ABSTRACT

Isoprenol (3-methyl-3-buten-1-ol) is a precursor to aviation fuels and other commodity chemicals and can be microbially synthesized from renewable carbon streams. Its production has been demonstrated in Pseudomonas putida KT2440 but its titers, rates, and yields have yet to reach commercially viable levels, potentially due to toxicity to the bacterial chassis. We hypothesized that utilization of Tolerization Adaptive Laboratory Evolution (TALE) would generate P. putida hosts more tolerant to isoprenol and suitable for enhanced production phenotypes. Here, we performed a comprehensive TALE campaign using three strains, the wild-type and two strains lacking subsets of known isoprenol catabolism and transport functions in quadruplicate independently evolved lineages. Several evolved clones from each starting strain displayed robust growth (up to 0.2 h-1) at 8 g/L of isoprenol, where starting strains could not grow. Whole genome resequencing of the 12 independent strain lineages identified convergent mutations. Reverse engineering each of the four commonly mutated regions individually (gnuR, ttgB-PP_1394, PP_3024-PP_5558, PP_1695) resulted in a partial recovery of the tolerance phenotypes observed in the evolved strains. Additionally, a proteomics-guided deletion of the master motility regulator, fleQ, in an evolved clone alleviated the tolerance vs. production trade-off, restoring isoprenol titers and consumption to levels observed in the starting strains. Collectively, this work demonstrated that an integrated strategy of laboratory evolution and rational engineering was effective to develop robust biofuel production hosts with minimized product toxicity.

PMID:40398593 | DOI:10.1016/j.ymben.2025.05.007

Curr Biol. 2025 May 15:S0960-9822(25)00562-7. doi: 10.1016/j.cub.2025.04.065. Online ahead of print.

ABSTRACT

Plants rely on water and light for photosynthesis, but water droplets on leaves can focus light into high-intensity spots, risking photodamage. Excessive light can impair growth or induce cell death, making it essential for plants to detect and respond to light fluctuations. While Ca2+ signaling has been linked to high light (HL) acclimation, the subcellular dynamics remain unclear. Here, we investigate Ca2+ responses to HL exposure in Arabidopsis thaliana. Using a glass bead to simulate light-focusing by water droplets, a biphasic increase of Ca2+ concentration was detected in the chloroplast stroma by the genetically encoded calcium indicator YC3.6 and confirmed using a newly established stroma-localized R-GECO1 (NTRC-R-GECO1). The stromal response was largely independent of light wavelength and unaffected in phot1 phot2 and cry1 cry2 mutants. Chemical inhibition of photosynthetic electron transport, microscopy-based Fv/Fm experiments, and measurement of the reactive oxygen species (ROS)-redox balance with roGFP-based reporters and Singlet Oxygen Sensor Green (SOSG) chemical dye suggested that photodamage and singlet oxygen contribute to the stromal Ca2+ response. While blue and white light also triggered a Ca2+ response in the cytosol and nucleus, pharmacological inhibition with cyclopiazonic acid (CPA) and loss-of-function mutants of the Ca2+ transporters BIVALENT CATION TRANSPORTER 2 (BICAT2) and endoplasmic reticulum (ER)-type Ca2+-ATPase (ECA) suggested that the HL response depends on a Ca2+ exchange between the ER and chloroplast stroma. The response was primarily light dependent but accelerated by increasing external temperature. This study implicates a novel Ca2+-mediated acclimation mechanism to HL stress, a process of growing relevance in the context of climate change.

PMID:40398414 | DOI:10.1016/j.cub.2025.04.065

EBioMedicine. 2025 May 20;116:105772. doi: 10.1016/j.ebiom.2025.105772. Online ahead of print.

ABSTRACT

BACKGROUND: Opportunistic premise plumbing pathogens (OPPPs) can establish reservoirs in hospital plumbing and cause healthcare associated infections (HAIs). There is currently no widely accepted protocol for sink drain cleaning to reduce OPPP burden.

METHODS: We implemented a sink cleaning intervention in 12 intensive care unit (ICU) rooms. At low frequency (1×/week) and high frequency (5×/week) intervals, we wiped sink surfaces with 10% bleach wipes and pumped a foamed preacid disinfectant into sink drains. We also maintained untreated rooms (0×/week). We used E-swabs to sample sink drains and surrounding surfaces during one baseline, two intervention, and two post-intervention periods over 23 months. Samples were selectively cultured for bacterial growth and antimicrobial resistant organism (ARO) isolation. We conducted whole-genome sequencing (WGS) on Pseudomonas spp. and Stenotrophomonas spp. isolates to track impacts on reservoirs over time. We also collected and analysed clinical isolates from patients occupying the study rooms and information about HAIs that occurred.

FINDINGS: The intervention reduced the proportion of sink drains yielding Gram-negative bacteria by up to 85% (95% CI: 56-114%) in high frequency rooms versus the baseline period, but this was not significant in low frequency rooms. It also reduced the proportion of sink drains yielding Pseudomonas spp. and Stenotrophomonas spp. by up to 100% (95% CI: 79-121%) and 95% (95% CI: 65-125%) versus the baseline period in high frequency rooms and up to 71% (95% CI: 50-92%, p < 0.001) and 66% (95% CI: 27-105%, p < 0.05) in low frequency rooms, respectively. WGS showed strains of Pseudomonas aeruginosa and Stenotrophomonas maltophilia that colonised sink drains for over 3 years across two studies. Following the intervention periods, P. aeruginosa reservoirs were replaced with new strains, while S. maltophilia reservoirs returned with the same strains.

INTERPRETATION: This environmental hygiene intervention may be effective in reducing the burden of OPPPs in hospital sinks.

FUNDING: Agency for Healthcare Research and Quality (R01HS027621), National Institute of Allergy and Infectious Diseases (U01AI123394, 1K23AI137321), Barnes-Jewish Hospital Foundation (5102), Washington University Institute of Clinical and Translational Sciences (4462) from the National Center for Advancing Translational Sciences (UL1TR002345).

PMID:40398352 | DOI:10.1016/j.ebiom.2025.105772

Exp Mol Pathol. 2025 May 20;143:104973. doi: 10.1016/j.yexmp.2025.104973. Online ahead of print.

ABSTRACT

BACKGROUND: The incidence of malignant pleural mesothelioma (MPM) has surged due to widespread asbestos exposure, particularly since the mid-20th century. Despite significant advancements in cancer treatment, an effective cure for MPM remains elusive, largely due to a limited understanding of the molecular mechanisms underlying asbestos-related carcinogenesis. This exploratory study aims to uncover gene expression patterns uniquely altered in mesothelioma patients with documented asbestos exposure, providing a solid foundation for future research focused on identifying novel prognostic and predictive biomarkers.

METHODS: Publicly available RNA sequencing data were analyzed through a bioinformatics pipeline to perform differential gene expression analysis. Additionally, functional enrichment analysis was applied to highlight significantly enriched Gene Ontology (GO) terms related to biological processes, molecular functions, and cellular components, offering insights into the molecular pathways involved in MPM development.

RESULTS: The analysis uncovered a set of differentially expressed genes (DEGs) in MPM patients with documented asbestos exposure, as well as key GO terms. These enriched biological terms reflect processes such as ion homeostasis and oxidative stress response, providing crucial information on the cellular alterations driven by asbestos exposure.

CONCLUSION: This study's findings deepen our understanding of the molecular landscape underlying asbestos-induced carcinogenesis in MPM. The identification of specific DEGs and enriched GO terms lays the foundation for future investigations, including the development of biomarkers, with potential implications for the diagnostic and prognostic assessment of MPM.

PMID:40398085 | DOI:10.1016/j.yexmp.2025.104973

J Cardiothorac Surg. 2025 May 22;20(1):234. doi: 10.1186/s13019-025-03486-8.

ABSTRACT

BACKGROUND: Ascorbic acid is an essential cofactor of catecholamine synthesis that increases capillary bed density and improves microcirculation perfusion. We hypothesized early ascorbic acid administration in cardiothoracic surgery would preserve the microcirculatory integrity and minimize postoperative vasoplegia.

METHODS: This was a single-arm pilot feasibility study of adults undergoing septal myectomy combined with valve intervention or alone using cardiopulmonary bypass. Intravenous ascorbic acid 1,500 mg was administered before and immediately following cardiopulmonary bypass and every 6 h after for 12 doses. Three historical controls were identified and matched to each trial participant on age, gender, body mass index, preoperative ejection fraction, surgery performed, and time on cardiopulmonary bypass. The feasibility endpoint was a composite of successful and timely 1) ascorbic acid administration, 2) laboratory assessment, and 3) microcirculation measurements across the perioperative phases of care. Clinical endpoints included vasoplegia incidence, acute kidney injury, and lengths of stay compared to controls.

RESULTS: Fifteen patients were enrolled and compared to 45 historically matched controls. Participants' median baseline plasma ascorbic acid concentration was 0.5 (0.3, 0.9) mg/dL. Four (27%) patients had suboptimal concentrations. Eleven participants (75%) did not meet the feasibility composite endpoint due to the inability of microcirculation measurement. Incidence of vasoplegia and acute kidney injury, vasopressor duration, and lengths of stay were similar between participants and historical controls. No drug-related adverse events were noted.

CONCLUSIONS: Timely microcirculation measurements were challenging in the complex cardiothoracic surgery environment. Compared to historical controls, no meaningful differences in clinical endpoints were noted with ascorbic acid treatment. The utility of ascorbic acid on post-cardiopulmonary bypass vasoplegia remains unclear.

TRIAL REGISTRATION: ClinicalTrials.gov (NCT03744702, registered on November 14, 2018).

PMID:40400032 | DOI:10.1186/s13019-025-03486-8

BMJ Open. 2025 May 21;15(5):e088512. doi: 10.1136/bmjopen-2024-088512.

ABSTRACT

OBJECTIVE: Drug-induced ischaemic colitis is a significant adverse event (AE) in clinical practice. This study aimed to recognise the top drugs associated with the risk of ischaemic colitis based on the FDA Adverse Event Reporting System (FAERS) database.

DESIGN: A cross-sectional design.

SETTING: All data retrieved from the FAERS database from the first quarter of 2004 to the fourth quarter of 2023.

PARTICIPANTS: A total of 5664 drug-induced ischaemic colitis AEs eligible for screening.

PRIMARY AND SECONDARY OUTCOME MEASURES: The Medical Dictionary for Regulatory Activities was used to identify ischaemic colitis (code: 10009895) cases. Disproportionality analysis for drug-associated ischaemic colitis signals.

RESULTS: Drug-induced ischaemic colitis AEs were more prevalent in females (60.12%) and individuals aged ≥65 years (34.25%). The common outcomes were hospitalisation (46.85%) and death (9.73%). Disproportionality analysis identified 91 ischaemic colitis signals and the top 30 drugs mainly involved in the gastrointestinal and nervous systems. The top five drugs with the highest reported OR, proportional reporting ratio, information component and the empirical Bayesian geometric mean, were alosetron, tegaserod, osmoprep, naratriptan and kayexalate. Additionally, 20 of the top 30 drugs did not have ischaemic colitis risk indicated in the package insert.

CONCLUSIONS: This study identified key drugs associated with ischaemic colitis, particularly alosetron, tegaserod, osmoprep, naratriptan and kayexalate. Notably, two-thirds of these drugs lacked ischaemic colitis warnings in their package inserts. These findings underscore the need for greater clinical vigilance, improved regulatory oversight and further research to clarify underlying mechanisms and support safer medication use.

PMID:40398943 | DOI:10.1136/bmjopen-2024-088512

Biochim Biophys Acta Mol Basis Dis. 2025 May 19:167913. doi: 10.1016/j.bbadis.2025.167913. Online ahead of print.

ABSTRACT

Mammalian target of rapamycin (mTOR) signaling constitutes a crucial intracellular signaling pathway indispensable for regulating a variety of pathophysiological processes, including cancers. Intriguingly, the inhibition of mTOR can reverse the adverse effects induced by chemotherapy drugs; however, the fundamental mechanism underlying this phenomenon remains unclear. In this study, we demonstrate that mTOR signaling blockade can mitigate etoposide- or cisplatin-induced intestinal injury in mice. The mTOR inhibitor AZD8055 can inhibit chemotherapy drug-induced normal cell pyroptosis, as manifested by a decreased proportion of PI-positive cells, attenuated intestinal cell swelling, and reduced release of lactate dehydrogenase (LDH) and high mobility group box-1 protein (HMGB1). We further determined that mTOR inhibition suppressed the cleavage of caspase-3 and gasdermin E (GSDME), suggesting the inhibition of the caspase3/GSDME signaling pathway. We also discovered that AZD8055 can impede chemotherapy drug-induced alterations in mitochondrial membrane potential, reactive oxygen species generation, and DNA damage in intestinal cells, which are the key upstream events for activating caspase-3. Correspondingly, data from in vivo mouse models also demonstrated that AZD8055 effectively curtailed intestinal DNA damage and inflammation induced by chemotherapy drugs. Importantly, although AZD8055 counteracts the side effects of chemotherapy drugs, it does not substantially affect their anti-tumor activity. Our study proposes the potential application of mTOR inhibitors as chemoprotective agents, presenting a means to prolong the duration of chemotherapy drug use and optimize the chemotherapeutic regimen.

PMID:40398827 | DOI:10.1016/j.bbadis.2025.167913

Curr Rheumatol Rep. 2025 May 21;27(1):24. doi: 10.1007/s11926-025-01189-6.

ABSTRACT

PURPOSE OF THIS REVIEW: Rare diseases, although individually infrequent, collectively impact a substantial number of people. Collaborative translational research using biospecimens is essential for advancing our understanding of the diverse characteristics and pathophysiology of rare diseases. Biobanks play a pivotal role in this endeavor by collecting, processing, transporting, and storing biospecimens, thereby serving as invaluable resources for medical research. In this review, we explore currently available biobanks, with a specific focus on those dedicated to rare rheumatic diseases. We also examine accessible best practice guidelines for establishing and maintaining high-quality biobanks, discuss the limitations and propose future directions for enhancing biobanking efforts in rare disease research.

RECENT FINDINGS: Advances in molecular and genomic technologies have expanded the role of biobanks, enhancing biomarker discovery and precision medicine. However, despite growth in biobanking capabilities, key challenges persist concerning ethics, interoperability, and biospecimen exchange, prompting active responses by various regulatory and governing bodies. Biobanking has transformed rare disease research. Strengthening national and international collaborations is essential for driving progress in this field and accelerating the development of novel therapeutic and precision medicine approaches.

PMID:40397074 | DOI:10.1007/s11926-025-01189-6

Ther Drug Monit. 2025 May 21. doi: 10.1097/FTD.0000000000001340. Online ahead of print.

ABSTRACT

Acute exacerbations of cystic fibrosis (CF) diminish quality of life and, if inadequately treated, can be life-threatening. The pathophysiological alterations associated with CF result in modified antibiotic pharmacokinetics. Moreover, the viscous mucus in the lungs limits pathogen exposure to drugs, rendering successful antibiotic treatment challenging. A 23-year-old female patient with CF was repeatedly admitted for intravenous antibiotic therapy for acute exacerbation of Pseudomonas aeruginosa infection. In the context of altered pharmacokinetics in CF, therapeutic drug monitoring of meropenem and piperacillin revealed consistently low plasma levels of both drugs. Targeted plasma levels were ultimately achieved through continuous high-dose infusions, based on therapeutic drug monitoring and subsequent dose adjustments.

PMID:40397762 | DOI:10.1097/FTD.0000000000001340

Eur J Radiol. 2025 May 14;189:112167. doi: 10.1016/j.ejrad.2025.112167. Online ahead of print.

ABSTRACT

OBJECTIVE: To assess the clinical value of the deep learning image reconstruction (DLIR) algorithm compared with conventional adaptive statistical iterative reconstruction-Veo (ASiR-V) in image quality, diagnostic confidence, and intestinal lesion detection in 100-kVp CT enterography (CTE) for patients with wide-range body mass index (BMI).

METHODS: A total of 84 patients underwent 100-kVp dual-phase CTE were included. Images were reconstructed using filtered back projection (FBP), ASiR-V 30 %, ASiR-V 60 %, and DLIR with low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H). The CT value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of small and large intestines were compared using repeated measures analysis of variance with the Bonferroni correction or Friedman test. The correlation between relative CNR increment and BMI was analyzed using Pearson's correlation coefficient. The overall image quality and diagnostic confidence scores were evaluated. Additionally, lesion detection of intestinal disease was conducted by three readers with different experience and compared between DLIR-M and ASiR-V 60 % images using McNemar's test.

RESULTS: SD decreased sequentially from FBP, ASiR-V 30 %, DLIR-L, ASiR-V 60 %, DLIR-M, to DLIR-H, which corresponded with improvements in CNR and SNR (all p < 0.001). The relative CNR increment of DLIR exhibited a significantly positive linear correlation with BMI (r:0.307-0.506, all p ≤ 0.005). For overall image quality scores, the ranking was: FBP < ASiR-V 30 % < ASiR-V 60 % ≈DLIR-L < DLIR-M ≈ DLIR-H. DLIR-M outperformed ASiR-V 60 % in diagnostic confidence (p ≤ 0.018 for all three readers). In lesion detection, for the two junior readers, DLIR-M exhibited higher sensitivity for inflammatory lesions compared to ASiR-V 60 % (0.700 (95 % confidence interval [95 % CI]: 0.354-0.919) vs. 0.300 (95 % CI: 0.081-0.646) for reader 1 and 0.700 (95 %CI: 0.354-0.919) vs. 0.500 (95 % CI: 0.201-0.799) for reader 2), though no statistical significance was reached.

CONCLUSION: DLIR effectively reduces noise and improves image quality in 100-kVp dual-phase CTE for wide-range BMIs. DLIR-M exhibits superior performance in image quality and diagnostic confidence, also provide potential value in improving intestinal inflammatory lesion detection in junior readers and sheds lights on benefiting clinical decision making, which needs further investigation.

PMID:40398003 | DOI:10.1016/j.ejrad.2025.112167

JMIR Med Inform. 2025 May 21;13:e64592. doi: 10.2196/64592.

ABSTRACT

BACKGROUND: Long-term ventilator-dependent patients often face problems such as decreased quality of life, increased mortality, and increased medical costs. Respiratory therapists must perform complex and time-consuming ventilator weaning assessments, which typically take 48-72 hours. Traditional disengagement methods rely on manual evaluation and are susceptible to subjectivity, human errors, and low efficiency.

OBJECTIVE: This study aims to develop an artificial intelligence-based prediction model to predict whether a patient can successfully pass a spontaneous breathing trial (SBT) using the patient's clinical data collected before SBT initiation. Instead of comparing different SBT strategies or analyzing their impact on extubation success, this study focused on establishing a data-driven approach under a fixed SBT strategy to provide an objective and efficient assessment tool. Through this model, we aim to enhance the accuracy and efficiency of ventilator weaning assessments, reduce unnecessary SBT attempts, optimize intensive care unit resource usage, and ultimately improve the quality of care for ventilator-dependent patients.

METHODS: This study used a retrospective cohort study and developed a novel deep learning architecture, hybrid CNN-MLP (convolutional neural network-multilayer perceptron), for analysis. Unlike the traditional CNN-MLP classification method, hybrid CNN-MLP performs feature learning and fusion by interleaving CNN and MLP layers so that data features can be extracted and integrated at different levels, thereby improving the flexibility and prediction accuracy of the model. The study participants were patients aged 20 years or older hospitalized in the intensive care unit of a medical center in central Taiwan between January 1, 2016, and December 31, 2022. A total of 3686 patients were included in the study, and 6536 pre-SBT clinical records were collected before each SBT of these patients, of which 3268 passed the SBT and 3268 failed.

RESULTS: The model performed well in predicting SBT outcomes. The training dataset's precision is 99.3% (2443/2460 records), recall is 93.5% (2443/2614 records), specificity is 99.3% (2597/2614 records), and F1-score is 0.963. In the test dataset, the model maintains accuracy with a precision of 89.2% (561/629 records), a recall of 85.8% (561/654 records), a specificity of 89.6% (586/654 records), and an F1-score of 0.875. These results confirm the reliability of the model and its potential for clinical application.

CONCLUSIONS: This study successfully developed a deep learning-based SBT prediction model that can be used as an objective and efficient ventilator weaning assessment tool. The model's performance shows that it can be integrated into clinical workflow, improve the quality of patient care, and reduce ventilator dependence, which is an important step in improving the effectiveness of respiratory therapy.

PMID:40397953 | DOI:10.2196/64592

J Med Internet Res. 2025 May 21;27:e73601. doi: 10.2196/73601.

ABSTRACT

BACKGROUND: During the COVID-19 pandemic, the continuous spread of misinformation on the internet posed an ongoing threat to public trust and understanding of epidemic prevention policies. Although the pandemic is now under control, information regarding the risks of long-term COVID-19 effects and reinfection still needs to be integrated into COVID-19 policies.

OBJECTIVE: This study aims to develop a robust and generalizable deep learning framework for detecting misinformation related to the prolonged impacts of COVID-19 by integrating pretrained language models (PLMs) with an innovative fuzzy rank-based ensemble approach.

METHODS: A comprehensive dataset comprising 566 genuine and 2361 fake samples was curated from reliable open sources and processed using advanced techniques. The dataset was randomly split using the scikit-learn package to facilitate both training and evaluation. Deep learning models were trained for 20 epochs on a Tesla T4 for hierarchical attention networks (HANs) and an RTX A5000 (for the other models). To enhance performance, we implemented an ensemble learning strategy that incorporated a reparameterized Gompertz function, which assigned fuzzy ranks based on each model's prediction confidence for each test case. This method effectively fused outputs from state-of-the-art PLMs such as robustly optimized bidirectional encoder representations from transformers pretraining approach (RoBERTa), decoding-enhanced bidirectional encoder representations from transformers with disentangled attention (DeBERTa), and XLNet.

RESULTS: After training on the dataset, various classification methods were evaluated on the test set, including the fuzzy rank-based method and state-of-the-art large language models. Experimental results reveal that language models, particularly XLNet, outperform traditional approaches that combine term frequency-inverse document frequency features with support vector machine or utilize deep models like HAN. The evaluation metrics-including accuracy, precision, recall, F1-score, and area under the curve (AUC)-indicated a clear performance advantage for models that had a larger number of parameters. However, this study also highlights that model architecture, training procedures, and optimization techniques are critical determinants of classification effectiveness. XLNet's permutation language modeling approach enhances bidirectional context understanding, allowing it to surpass even larger models in the bidirectional encoder representations from transformers (BERT) series despite having relatively fewer parameters. Notably, the fuzzy rank-based ensemble method, which combines multiple language models, achieved impressive results on the test set, with an accuracy of 93.52%, a precision of 94.65%, an F1-score of 96.03%, and an AUC of 97.15%.

CONCLUSIONS: The fusion of ensemble learning with PLMs and the Gompertz function, employing fuzzy rank-based methodology, introduces a novel prediction approach with prospects for enhancing accuracy and reliability. Additionally, the experimental results imply that training solely on textual content can yield high prediction accuracy, thereby providing valuable insights into the optimization of fake news detection systems. These findings not only aid in detecting misinformation but also have broader implications for the application of advanced deep learning techniques in public health policy and communication.

PMID:40397945 | DOI:10.2196/73601

PLoS One. 2025 May 21;20(5):e0323495. doi: 10.1371/journal.pone.0323495. eCollection 2025.

ABSTRACT

Previous studies using the conventional Hedonic Price Model to predict existing housing prices may have limitations in addressing the relationship between house prices and streetscapes as visually perceived at the human eye level, due to the constraints of streetscape estimations. Therefore, in this study, we analyzed the relationship between streetscapes visually perceived at eye level and single-family home prices in Seoul, Korea, using computer vision technology and machine learning algorithms. We used transaction data for 13,776 single-family housing sales between 2017 and 2019. To measure visually perceived streetscapes, this study used the Deeplab V3 + deep-learning model with 233,106 Google Street View panoramic images. Then, the best machine-learning model was selected by comparing the explanatory powers of the hedonic price model and all alternative machine-learning models. According to the results, the Gradient Boost model, a representative ensemble machine learning model, performed better than XGBoost, Random Forest, and Linear Regression models in predicting single-family house prices. In addition, this study used an interpretable machine learning model of the SHAP method to identify key features that affect single-family home price prediction. This solves the "black box" problem of machine learning models. Finally, by analyzing the nonlinear relationship and interaction effects between perceived streetscape characteristics and house prices, we easily and quickly identified the relationship between variables the hedonic price model partially considers.

PMID:40397916 | DOI:10.1371/journal.pone.0323495

PLoS One. 2025 May 21;20(5):e0323478. doi: 10.1371/journal.pone.0323478. eCollection 2025.

ABSTRACT

In recent decades, automatic train operation (ATO) systems have been gradually adopted by many metro systems, primarily due to their cost-effectiveness and practicality. However, a critical examination reveals computational constraints, adaptability to unforeseen conditions and multi-objective balancing that our research aims to address. In this paper, expert knowledge is combined with deep reinforcement learning algorithm (Proximal Policy Optimization, PPO) and two enhanced intelligent train operation algorithms (EITO) are proposed. The first algorithm, EITOE, is based on an expert system containing expert rules and a heuristic expert inference method. On the basis of EITOE, we propose EITOP algorithm using the PPO algorithm to optimize multiple objectives by designing reinforcement learning strategies, rewards, and value functions. We also develop the double minimal-time distribution (DMTD) calculation method in the EITO implementation to achieve longer coasting distances and further optimize the energy consumption. Compared with previous works, EITO enables the control of continuous train operation without reference to offline speed profiles and optimizes several key performance indicators online. Finally, we conducted comparative tests of the manual driving, intelligent driving algorithm (ITOR, STON), and the algorithms proposed in this paper, EITO, using real line data from the Yizhuang Line of Beijing Metro (YLBS). The test results show that the EITO outperform the current intelligent driving algorithms and manual driving in terms of energy consumption and passengers' comfort. In addition, we further validated the robustness of EITO by selecting some complex lines with speed limits, gradients and different running times for testing on the YLBS. Overall, the EITOP algorithm has the best performance.

PMID:40397887 | DOI:10.1371/journal.pone.0323478