PNAS Nexus. 2025 Jan 16;4(1):pgaf008. doi: 10.1093/pnasnexus/pgaf008. eCollection 2025 Jan.

ABSTRACT

While more data are becoming available on gene activity at different levels of biological organization, our understanding of the underlying biology remains incomplete. Here, we introduce a metabolic efficiency framework that considers highly expressed proteins (HEPs), their length, and biosynthetic costs in terms of the amino acids (AAs) they contain to address the observed balance of expression costs in cells, tissues, and cancer transformation. Notably, the combined set of HEPs in either cells or tissues shows an abundance of large and costly proteins, yet tissues compensate this with short HEPs comprised of economical AAs, indicating a stronger tendency toward mitigating costs. We additionally observe that short proteins are prevalent HEPs across individual cells and tissues, whereas long ones are more specific. Furthermore, the precise proportion of short, long, economical, or costly HEP classes indicates that particular cell types and tissues align more closely with the metabolic efficiency model, with some tissues displaying behavior akin to their constituent cells. Finally, tumors typically increase the production of short and low-cost HEPs compared with matched normal tissues, while genes that decrease their high expression levels in tumors often tend to be associated with high costs. Overall, the metabolic efficiency framework serves as a useful simplifying model for interpreting genome-wide expression data across scales.

PMID:39867669 | PMC:PMC11759310 | DOI:10.1093/pnasnexus/pgaf008

Bioinform Adv. 2025 Jan 21;5(1):vbae209. doi: 10.1093/bioadv/vbae209. eCollection 2025.

ABSTRACT

MOTIVATION: Missing values are prevalent in high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. The three missingness patterns, including missing completely at random, missing at random, and missing not at random, describe unique dependencies between the missing and observed data. The optimal imputation method for each dataset depends on the type of data, the cause of the missingness, and the nature of relationships between the missing and observed data. The challenge is to identify the optimal imputation solution for a given dataset.

RESULTS: ImpLiMet: is a user-friendly web-platform that enables users to impute missing data using eight different methods. For a given dataset, ImpLiMet suggests the optimal imputation solution through a grid search-based investigation of the error rate for imputation across three missingness data simulations. The effect of imputation can be visually assessed by histogram, kurtosis, and skewness, as well as principal component analysis comparing the impact of the chosen imputation method on the distribution and overall behavior of the data.

AVAILABILITY AND IMPLEMENTATION: ImpLiMet is freely available at https://complimet.ca/shiny/implimet/ and https://github.com/complimet/ImpLiMet.

PMID:39867531 | PMC:PMC11761345 | DOI:10.1093/bioadv/vbae209

ACS Cent Sci. 2025 Jan 3;11(1):25-35. doi: 10.1021/acscentsci.4c01437. eCollection 2025 Jan 22.

ABSTRACT

Antimicrobial drug resistance (AMR) is a pressing global human health challenge. Humans face one of their grandest challenges as climate change expands the habitat of vectors that bear human pathogens, incidences of nosocomial infections rise, and new antibiotics discovery lags. AMR is a multifaceted problem that requires a multidisciplinary and an "all-hands-on-deck" approach. As chemical microbiologists, we are well positioned to understand the complexities of AMR while seeing opportunities for tackling the challenge. In this Outlook, we focus on vulnerabilities of human pathogens and posit that they represent "opportunity targets" for which few modulatory ligands exist. We center our attention on proteins in Gram-negative organisms, which are recalcitrant to many antibiotics because of their external membrane barrier. Our hope is to highlight such targets and explore their potential as "druggable" proteins for infectious disease mitigation. We posit that success in this endeavor will introduce new classes of antibiotics that might alleviate some of the current pressing AMR concerns.

PMID:39866699 | PMC:PMC11758222 | DOI:10.1021/acscentsci.4c01437

Heliyon. 2025 Jan 2;11(1):e41633. doi: 10.1016/j.heliyon.2025.e41633. eCollection 2025 Jan 15.

ABSTRACT

Isoxazole and oxadiazole derivatives inhibiting 3-hydroxykynurenine transaminase (3HKT) are potential larvicidal candidates. This study aims to identify more suited potential inhibitors of Anopheles gambiae 3HKT (Ag3HKT) through molecular docking and molecular dynamics simulation. A total of 958 compounds were docked against Anopheles gambiae 3HKT (PDB ID: 2CH2) using Autodock vina and Autodock4. The top three identified hits were subjected to 300 ns molecular dynamics simulation using AMBER 18 and ADMET analysis using SWISSADME predictor and ADMETSAR. Replacement of alkyl attachment on C5 of isoxazole or oxadiazole derivative with a cycloalkyl group yielded compounds with lower binding energy than their straight chain counterparts. The top three compounds were brominated compounds, 2-[3-(4-bromophenyl)-1,2-oxazol-5-yl]cyclopentane-1-carboxylic acid, 2-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, 3-[3-(4-bromo-2-methylphenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, and they had binding energies of -8.58, -8.25, and -8.18 kcal/mol in virtual screening against 2CH2 protein target, respectively. These compounds were predicted to be less toxic than temephos, a standard larvicide and more biodegradable than previously reported inhibitors. The three compounds exhibited a greater stabilizing effect on 2CH2 protein target than 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid, a previously reported inhibitor candidate with good larvicidal activity on Aedes aegypti. Further thermodynamic calculations revealed that the top three compounds possessed total binding energies (ΔGbind) of -26.64 kcal/mol, -24.26 kcal/mol and -14.11 kcal/mol, respectively, as compared to -12.02 kcal/mol for 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid. These compounds could be better larvicides than previously reported isoxazole or oxadiazole derivatives and safer than temephos.

PMID:39866405 | PMC:PMC11759636 | DOI:10.1016/j.heliyon.2025.e41633

Development. 2025 Jan 27:dev.204397. doi: 10.1242/dev.204397. Online ahead of print.

ABSTRACT

Numerous regulators of cardiomyocyte (CM) proliferation have been identified, yet how they coordinate during cardiac development or regeneration is poorly understood. Here, we developed a computational model of the CM proliferation regulatory network to obtain key regulators and systems-level understanding. The model defines five modules (DNA replication, mitosis, cytokinesis, growth factor, Hippo pathway) and integrates them into a network of 72 nodes and 88 reactions that correctly predicts 73 of 78 (93.6%) independent experiments from the literature. The model predicts that in response to YAP activation, the Hippo module crosstalks to the growth factor module via PI3K and cMyc to drive cell cycle activity. This predicted YAP-cMyc axis is validated experimentally in rat cardiomyocytes and further supported by YAP-stimulated cMyc open chromatin and mRNA in mouse hearts. This validated computational model predicts how individual regulators and modules coordinate to control CM proliferation.

PMID:39866065 | DOI:10.1242/dev.204397

Plant J. 2025 Jan;121(2):e17210. doi: 10.1111/tpj.17210.

ABSTRACT

Bacterial wilt caused by Ralstonia solanacearum is a devastating disease affecting a great many crops including peanut. The pathogen damages plants via secreting type Ш effector proteins (T3Es) into hosts for pathogenicity. Here, we characterized RipAU was among the most toxic effectors as ΔRipAU completely lost its pathogenicity to peanuts. A serine residue of RipAU is the critical site for cell death. The RipAU targeted a subtilisin-like protease (AhSBT1.7) in peanut and both protein moved into nucleus. Heterotic expression of AhSBT1.7 in transgenic tobacco and Arabidopsis thaliana significantly improved the resistance to R. solanacearum. The enhanced resistance was linked with the upregulating ERF1 defense marker genes and decreasing pectin methylesterase (PME) activity like PME2&4 in cell wall pathways. The RipAU played toxic effect by repressing R-gene, defense hormone signaling, and AhSBTs metabolic pathways but increasing PMEs expressions. Furthermore, we discovered AhSBT1.7 interacted with AhPME4 and was colocalized at nucleus. The AhPME speeded plants susceptibility to pathogen via mediated cell wall degradation, which inhibited by AhSBT1.7 but upregulated by RipAU. Collectively, RipAU impaired AhSBT1.7 defense for pathogenicity by using PME-mediated cell wall degradation. This study reveals the mechanism of RipAU pathogenicity and AhSBT1.7 resistance, highlighting peanut immunity to bacterial wilt for future improvement.

PMID:39866050 | DOI:10.1111/tpj.17210

Curr Neuropharmacol. 2025 Jan 24. doi: 10.2174/011570159X323666241029171256. Online ahead of print.

ABSTRACT

Epilepsy is a neurological disorder affecting millions of people worldwide. Antiseizure medications (ASM) remain a critical therapeutic intervention for treating epilepsy, notwithstanding the rapid development of other therapies. There have been substantial advances in epilepsy medications over the past three decades, with over 20 ASMs now available commercially. Here we describe the conventional and unique mechanisms of action of ASMs, focusing on everolimus, cannabidiol, cenobamate, fenfluramine, and ganaxolone, the five most recently marketed ASMs. Major obstacles in the development of ASMs are also addressed, particularly drug-resistant epilepsy as well as psychiatric and behavioral adverse effects of ASMs. Moreover, we delve into the mechanisms and comparative efficacy of ASM polytherapy, with remarks on the benefits and challenges in their application in clinical practice. In addition, the characteristics of the ideal ASM are outlined in this review. The review also discusses the development of new potential ASMs, including modifying existing ASMs to improve efficacy and tolerability. Furthermore, we expound on the modulation of γ- aminobutyric acid type A receptor (GABAAR) as a strategy for the treatment of epilepsy and the identification of a GABAAR agonist, isoguvacine, as a potential ASM.

PMID:39865817 | DOI:10.2174/011570159X323666241029171256

Front Immunol. 2025 Jan 10;15:1496095. doi: 10.3389/fimmu.2024.1496095. eCollection 2024.

ABSTRACT

PURPOSE: The α-FAtE score, composed of alpha-fetoprotein, alkaline phosphatase, and eosinophil levels, has been reported as a predictor of prognosis in hepatocellular carcinoma (HCC) patients treated with atezolizumab plus bevacizumab. This study aimed to investigate the predictive ability of α-FAtE score for the efficacy and safety of locoregional immunotherapy as the treatment of HCC patients.

METHODS AND PATIENTS: We conducted a retrospective study of 446 HCC patients at Sun Yat-sen University Cancer Center from January 1st 2019 to January 1st 2023. The predictive performance was evaluated by the concordance index, the area under the receiver operating characteristics curve, the Kaplan-Meier curve and multiple Cox regression analysis.

RESULTS: 446 patients were divided into the α-FAtE 0-1 group (n=211) and α-FAtE 2-3 group (n=235). The median progression-free survival(PFS) of the α-FAtE 0-1 group and 2-3 group was 7.3 months (95%CI 6.6-8.7 months), and 12.3 months (95% CI 10.4-14.1 months; P<0.001), respectively. The median overall survival (OS) of the α-FAtE 0-1 group and 2-3 group was 16.3 months (95%CI 13.7-21.5 months) and 34.1 months (95% CI 27.6-NA months; P<0.001), respectively. HCC patients in the α-FAtE 2-3 group had higher complete response (CR) rate and experienced less drug-related adverse events than those in the α-FAtE 0-1 group. Moreover, a lower α-FAtE score was identified as an independent prognostic indicator for both OS and PFS of advanced HCC patients receiving locoregional immunotherapy.

CONCLUSION: The α-FAtE score is a superior predictor of prognosis in HCC patients receiving locoregional immunotherapy, offering a valuable tool for patient stratification and treatment planning.

PMID:39867887 | PMC:PMC11757168 | DOI:10.3389/fimmu.2024.1496095

Perspect Clin Res. 2025 Jan-Mar;16(1):31-37. doi: 10.4103/picr.picr_20_24. Epub 2024 Aug 7.

ABSTRACT

AIM: The study aimed to determine the incidence of adverse drug reactions (ADRs) among newly diagnosed tuberculosis (TB) patients receiving daily drug regimen with fixed-dose combination treatment under the National Tuberculosis Elimination Program.

MATERIALS AND METHODS: A community-based prospective cohort study was carried out in the Udupi district. Over 12 months, all newly diagnosed TB patients of either gender were included from 63 primary health centers and 6 community health centers, and ADRs were recorded by personal interviews.

RESULTS: A total of 710 patients were enrolled, among whom 453 (63.8%), were males, and 257 (36.2%) were females. Pulmonary TB was diagnosed among 510 (71.8%) and 200 (28.2%) were extrapulmonary cases. During the intensive phase (IP) of treatment, 480 (67.6%) patients reported at least one ADR and 79 (11.1%) experienced two ADRs during IP and 31 (6.5%) had ADRs during the continuation phase. Out of 480, 140 (29.2%) had gastritis, 132 (27.5%) had vomiting, 105 (21.9%) had nausea, 60 (12.5%) had skin rashes, 27 (5.6%) had drug-induced hepatitis, and 16 (3.3%) had vision problems. Among 480 patients with ADRs, 462 (96.3%) had successful treatment outcomes, the remaining 17 patients (3.5%) died, and one (0.2%) had treatment failure.

CONCLUSIONS: Adverse events were more common in the 1st few months of treatment than in subsequent months. All mild-to-moderate ADRs were effectively managed, and most had successful treatment outcomes.

PMID:39867528 | PMC:PMC11759235 | DOI:10.4103/picr.picr_20_24

Comput Biol Med. 2025 Jan 25;186:109713. doi: 10.1016/j.compbiomed.2025.109713. Online ahead of print.

ABSTRACT

BACKGROUND: Ovarian cancer is among the most lethal gynecologic malignancy that threatens women's lives. Pathological diagnosis is a key tool for early detection and diagnosis of ovarian cancer, guiding treatment strategies. The evaluation of various ovarian cancer-related cells, based on morphological and immunohistochemical pathology images, is deemed an important step. Currently, the lack of a comprehensive deep learning framework for detecting various ovarian cells poses a performance bottleneck in ovarian cancer pathological diagnosis.

METHOD: This paper presents OCDet, an object detection model with channel attention, which achieves comprehensive detection of CD3, CD8, and CD20 positive lymphocytes in immunohistochemical pathology slides, and neutrophils and polyploid giant cancer cells in H&E slides of ovarian cancer. OCDet, utilizing CSPDarkNet as its backbone, incorporates an Efficient Channel Attention module for Resolution-Specified Embedding Refinement and Multi-Resolution Embedding Fusion, enabling the efficient extraction of pathological features.

RESULT: The experiment demonstrated that OCDet performed well in target detection of three types of positive lymphocytes in immunohistochemical images, as well as neutrophils and polyploid giant cancer cells in H&E images. The mAP@0.5 reached 98.82 %, 92.91 %, and 90.49 % respectively, all surpassing other compared models. The ablation experiment further highlighted the superiority of the introduced Efficient Channel Attention (ECA) mechanism.

CONCLUSION: The proposed OCDet enables accurate detection of multiple types of cells in immunohistochemical and morphological pathology images of ovarian cancer, serving as an efficient application tool for pathological diagnosis thereof. The proposed framework has the potential to be further applied to other cancer types.

PMID:39864335 | DOI:10.1016/j.compbiomed.2025.109713

Comput Biol Med. 2025 Jan 25;186:109720. doi: 10.1016/j.compbiomed.2025.109720. Online ahead of print.

ABSTRACT

Diabetes mellitus (DM) represents a major global health challenge, affecting a diverse range of demographic populations across all age groups. It has particular implications for women during pregnancy and the postpartum period. The contemporary prevalence of sedentary lifestyle patterns and suboptimal dietary practices has substantially contributed to the escalating incidence of this metabolic disorder. The timely identification of diabetes mellitus (DM) in the female population is crucial for preventing related complications and facilitating the implementation of effective therapeutic interventions. However, conventional predictive models frequently demonstrate limited external validity when applied across heterogeneous datasets, potentially compromising clinical utility. This study proposes a robust machine learning (ML) framework for diabetes prediction across diverse populations using two distinct datasets: the PIMA and BD datasets. The framework employs intra-dataset, inter-dataset, and partial fusion dataset validation techniques to comprehensively assess the generalizability and performance of various models. In intra-dataset validation, the Extreme Gradient Boosting (XGBoost) model achieved the highest accuracy on the PIMA dataset with 79%. In contrast, the Random Forest (RF) and Gradient Boosting (GB) models demonstrated accuracy close to 99% on the BD dataset. For inter-dataset validation, where models were trained on one dataset and tested on the other, the ensemble model outperformed others with 88% accuracy when trained on PIMA and tested on BD. However, model performance declined when trained on BD and tested on PIMA (74%), reflecting the challenges of inter-dataset generalization ability. Finally, during partial fusion data validation, the deep learning (DL) model achieved 74% accuracy when trained on the BD dataset augmented with 30% of the PIMA dataset. This accuracy increased to 98% when training on the PIMA dataset combined with 30% of the BD data. These findings emphasize the importance of dataset diversity and the partial fusion dataset that can significantly enhance the model's robustness and generalizability. This framework offers valuable insights into the complexities of diabetes prediction across heterogeneous environments.

PMID:39864329 | DOI:10.1016/j.compbiomed.2025.109720

J Hazard Mater. 2025 Jan 21;488:137325. doi: 10.1016/j.jhazmat.2025.137325. Online ahead of print.

ABSTRACT

Low levels of human norovirus (HuNoV) in food and environment present challenges for nucleic acid detection. This study reported an evaporation-enhanced hydrogel digital reverse transcription loop-mediated isothermal amplification (HD RT-LAMP) with interfacial enzymatic reaction for sensitive HuNoV quantification in food and water. By drying samples on a chamber array chip, HuNoV particles were enriched in situ. The interfacial amplification of nucleic acid at the hydrogel-chip interface was triggered after coating HD RT-LAMP system. Nanoconfined spaces in hydrogels provided a simple and rapid "digital format" to quantify single virus within 15 min. Through in situ evaporation for enrichment, the sensitivity level was increased by 20 times. The universality of the sensitivity-enhanced assay was also verified using other bacteria and virus. Furthermore, a deep learning model and smartphone app were developed for automatic amplicon analysis. Multiple actual samples, including 3 lake waters, strawberry, tap water and drinking water, were in situ enriched and detected for norovirus quantification using the chamber arrays. Therefore, the sensitivity-enhanced HD RT-LAMP is an efficient assay for testing biological hazards in food safety monitoring and environmental surveillance.

PMID:39864200 | DOI:10.1016/j.jhazmat.2025.137325

Zoology (Jena). 2025 Jan 20;168:126240. doi: 10.1016/j.zool.2025.126240. Online ahead of print.

ABSTRACT

Floods, which occur when the amount of precipitation surpasses the capacity of an area to drain it adequately, have detrimental consequences on the survival and future generations of fishes. However, few works have reported the prediction of this natural phenomenon in a relation to certain fish species, especially in fast-flowing rivers. In the specific context of the northern mountainous provinces of Vietnam, where the Spinibarbus sp. fish species resides, it has been observed through the current study that the fish population in Lang Son exhibits the lowest genetic diversity and genetic distance. Consequently, the population of Spinibarbus sp. in Lang Son shows a heightened susceptibility to floods, resulting in reduction in population size and compromised population resilience. In order to provide decision support information for managers, conservationists, and researchers, we have employed a genetic algorithm-support vector machine regression (GA-SVR) predictive model to map flood vulnerability using thirteen dependent variables. The study findings have unveiled a significant negative correlation between flood-sensitive regions and genetic diversity. These discoveries emphasize the significance of considering the impact of floods on the genetic diversity of Spinibarbus sp. in Lang Son through flood vulnerability mapping. This underscores the value of establishing a comprehensive framework based on the GA-SVR algorithm for early flood detection, thereby facilitating the implementation of effective measures to minimize damages and conserve this commercial fish species.

PMID:39864169 | DOI:10.1016/j.zool.2025.126240

Eur J Pharmacol. 2025 Jan 24:177304. doi: 10.1016/j.ejphar.2025.177304. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Psilocybin is a serotonergic psychedelic with therapeutic potential for several neuropsychiatric disorders, including depression and anxiety disorders. Serotonin-transporter (5-HTT) knockout mice (KO) are a well-validated mouse model of anxiety/depression and are relevant to both chronic treatment with serotonin transporter reuptake inhibitors (SSRIs) and polymorphisms in the serotonin transporter-linked polymorphic region (5-HTTLPR) associated with depression/anxiety and resistance to classic antidepressant treatments. However, there is yet to be a study assessing the effect of psilocybin in 5-HTT KO mice.

EXPERIMENTAL APPROACH: We investigated the effects of a single dose of psilocybin (1 mg/kg) on locomotor activity and the head-twitch response as well as anxiety- and depressive-like behaviour in KO versus wild-type (WT) mice using the light-dark box and Porsolt swim test respectively.

KEY RESULTS: We found that both the psilocybin-induced head-twitch and hyperlocomotor responses observed in WT mice were completely absent in KO animals. In female WT mice only, psilocybin was also able to block the weight loss observed one day after intraperitoneal injection. While psilocybin did not alter anxiety- and depression-like behaviours for both genotypes, we revealed a genotype-specific trend for a main effect of treatment for WT females (p = 0.054) in the Porsolt swim test. Finally, we found that only female KO mice exhibit anhedonia-like behaviour in the saccharin-preference test.

CONCLUSION AND IMPLICATIONS: Our findings highlight the complexity of psilocybin's effects and suggest that functional integrity of 5-HTT is essential for psilocybin's acute behavioural effects. This could also have implications for pharmacogenetics, including individuals with polymorphisms or mutations in 5-HTT.

PMID:39864573 | DOI:10.1016/j.ejphar.2025.177304

Otolaryngol Head Neck Surg. 2025 Jan 26. doi: 10.1002/ohn.1143. Online ahead of print.

ABSTRACT

OBJECTIVE: Cystic fibrosis (CF) is a clinical entity defined by aberrant chloride (Cl-) ion transport causing downstream effects on mucociliary clearance (MCC) in sinonasal epithelia. Inducible deficiencies in transepithelial Cl- transport via CF transmembrane conductance regulator (CFTR) has been theorized to be a driving process in recalcitrant chronic rhinosinusitis (CRS) in patients without CF. We have previously identified that brief exposures to bacterial lipopolysaccharide (LPS) in mammalian cells induces an acquired dysfunction of CFTR in vitro and in vivo. The objective of the current study is to evaluate whether LPS generates a model of acquired CFTR dysfunction murine nasal airways.

STUDY DESIGN: Basic science.

SETTING: Laboratory.

METHODS: CFTR+/+ murine nasal airways were irrigated with 2 µg/mL LPS or control vehicle twice daily for 1 week and transepithelial Cl- transport assessed with the nasal potential difference (NPD) assay. Histopathologic evaluation included the number of lymphoid aggregates, as well as the epithelial and subepithelial heights.

RESULTS: Transepithelial Cl- secretion by NPD was markedly reduced in mice exposed to LPS (in mV, -0.14 ± 7.7 vs control, -6.98 ± 7.15, P < .05), while amiloride-sensitive voltage was preserved (6.38 ± 5.09 vs control, 7.36 ± 2.87, P = .99). Histopathology demonstrated significantly higher lymphoid aggregates per high-power field (2.3 ± 0.9 vs 1.1 ± 0.7, control, P < .01) and increased epithelial height (in µm, 40.88 ± 13.9 vs control, 25.32 ± 6.26, P < .05).

CONCLUSION: Twice daily irrigation with LPS in murine nasal airways over 1 week led to acquired defects in transepithelial Cl- transport. This animal model provides an excellent means to test the contributions of acquired CFTR dysfunction to CRS and test CFTR correctors and potentiators that might improve MCC.

PMID:39865444 | DOI:10.1002/ohn.1143

J Cyst Fibros. 2025 Jan 25:S1569-1993(25)00009-8. doi: 10.1016/j.jcf.2025.01.009. Online ahead of print.

ABSTRACT

BACKGROUND: A previous Australia-wide pilot study identified pain as a significant burden in people with CF (pwCF). However, the prevalence, frequency and severity have not been evaluated using validated tools.

METHODS: Australian adults, pwCF and healthy controls (HC) were invited to complete an online questionnaire from July 2023 - February 2024, consisting of four validated tools: Brief Pain Inventory, Pain Catastrophising Scale, PAGI-SYM and PAC-SYM. The questionnaire, disseminated via Cystic Fibrosis Australia, CF Together and online social media groups, explored experiences surrounding pain and its management using closed and free text entries.

RESULTS: There were 206 respondents, consisting of 117 CF patients and 89 HC. Over 70 % (n = 69) of pwCF reported pain compared to 28 % (n = 21) of HC (p = <0.001). Further, significantly higher pain frequency per month was reported for pwCF than HC (40 % vs. 10 %; p < 0.001). Symptom clustering was also observed where at least three other locations of pain were reported, and pain was reported to trigger other physiological and psychological symptoms. Notably, there was no significant difference in the locations, occurrence, frequency or severity of pain between those on a CFTR modulator or not (p = 0.625). PwCF also reported significantly lower relief from over-the-counter therapies (p = 0.002) and expressed themes of unmet symptom and management needs.

CONCLUSIONS: This study identified a high prevalence of pain affecting multiple body parts in pwCF compared to HC and suggests that pain is sub-optimally managed, impairing their quality of life. Increased awareness and early recognition within the CF clinics and the development of clinical pathways are critically needed to better manage and monitor pain in pwCF, leading to improved quality of life and health outcomes.

PMID:39864974 | DOI:10.1016/j.jcf.2025.01.009

Microb Pathog. 2025 Jan 24:107332. doi: 10.1016/j.micpath.2025.107332. Online ahead of print.

ABSTRACT

Burkholderia contaminans SK875, a member of Burkholderia cepacia complex (Bcc), are known to cause lung infections in cystic fibrosis patients. To gain deeper insights into its quorum sensing (QS)-mediated pathogenicity, we employed a transposon (Tn) insertion-based random mutagenesis approach. A Tn mutant library comprising of 15,000 transconjugants was generated through conjugation between wild-type (WT) recipient B. contaminans SK875 and the donor E. coli BW20767 carrying pRL27 plasmid. From this library, 26 mutants were initially screened using the reporter strain Agrobacterium tumefaciens NT1, identified as blue-colored indicator colonies. These mutants were further analyzed for phenotypic variations related to autoinducer (AI) production, morphological changes, motility, biofilm formation, protease activity, and virulence in Caenorhabditis elegans. The Tn insertion sites in the mutants were sequenced and aligned with the reference genome of B. contaminans SK875 (PRJNA439184). Sequence analysis revealed the Tn5 insertion in genes encoding Ribonuclease P protein, a hypothetical protein, gamma-glutamyltranspeptidase 1, GCN5-related N-acetyltransferase (DUF1311), cytochrome C oxidase assembly protein, glutamyl-Q tRNA synthetase, AFG1-like ATPase, chorismate synthase, and aldehyde oxidase. Compared to wild-type (WT) strain B. contaminans SK875, the mutants (SK1917, SK1925, SK1926, SK1927, SK1935) exhibited attenuated AI production, impaired swimming and swarming motility, reduced biofilm formation and protease activity, and decreased virulence in C. elegans. We suggest that these genes are likely involved in the QS-dependent pathogenicity of B. contaminans. This study also introduces a visual color-screening method for identifying novel gene functions related to QS-dependent pathogenicity in Burkholderia species.

PMID:39864765 | DOI:10.1016/j.micpath.2025.107332

Respiration. 2025 Jan 24:1-16. doi: 10.1159/000543009. Online ahead of print.

ABSTRACT

INTRODUCTION: Elexacaftor/tezacaftor/ivacaftor (ETI) has shown significant improvements in pulmonary and nutritional status in persons with cystic fibrosis (pwCF). Less is known about the extrapulmonary impact of ETI and effects on airway microbiology, lung clearance index (LCI) and fraction of exhaled nitric oxide (FeNO).

METHODS: A multicentre prospective observational trial, including 79 pwCF ≥ 18 years eligible for ETI. Assessments were done at the initiation of, and 3 and 6 months into treatment with ETI. Outcomes included forced expiratory volume in 1 second (FEV1), LCI, FeNO, sputum or cough swab culture, body mass index (BMI), cystic fibrosis questionnaire-revised respiratory domain (CFQ-R RD), sinonasal outcome test-22 (SNOT-22), general anxiety disorder-7 (GAD-7), patient health questionnaire-9 (PHQ-9), fecal elastase-1 (FE-1), adherence to baseline therapies, exacerbation rate and adverse events.

RESULTS: Our cohort included 79 pwCF (31±11(SD) years) with a baseline ppFEV1 of 68±23. Forty-two (53%) pwCF were previously treated with a CFTR modulator. In the entire study group, there were significant improvements from baseline in ppFEV1, LCI, FeNO, annualized exacerbation rate, BMI, CFQ-R RD and SNOT-22 (p<0.05 for all). Airway culture positivity for methicillin-susceptible Staphylococcus aureus and Pseudomonas aeruginosa also decreased during the study period. There was no significant change in FE-1, GAD-7 or PHQ-9. Adherence to dornase alfa and hypertonic saline decreased.

CONCLUSION: ETI treatment led to significant improvements in respiratory and nutritional status, alongside a decrease in adherence to chronic supportive therapies. We did not observe any significant changes in exocrine pancreas function or in questionnaire scores for depression and anxiety.

PMID:39864420 | DOI:10.1159/000543009

United European Gastroenterol J. 2025 Jan 26. doi: 10.1002/ueg2.12723. Online ahead of print.

ABSTRACT

The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA-DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full-text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.

PMID:39865461 | DOI:10.1002/ueg2.12723

Eur J Nucl Med Mol Imaging. 2025 Jan 27. doi: 10.1007/s00259-024-07065-2. Online ahead of print.

ABSTRACT

PURPOSE: To develop and validate a prostate-specific membrane antigen (PSMA) PET/CT based multimodal deep learning model for predicting pathological lymph node invasion (LNI) in prostate cancer (PCa) patients identified as candidates for extended pelvic lymph node dissection (ePLND) by preoperative nomograms.

METHODS: [68Ga]Ga-PSMA-617 PET/CT scan of 116 eligible PCa patients (82 in the training cohort and 34 in the test cohort) who underwent radical prostatectomy with ePLND were analyzed in our study. The Med3D deep learning network was utilized to extract discriminative features from the entire prostate volume of interest on the PET/CT images. Subsequently, a multimodal model i.e., Multi kernel Support Vector Machine was constructed to combine the PET/CT deep learning features, quantitative PET and clinical parameters. The performance of the multimodal models was assessed using final histopathology as the reference standard, with evaluation metrics including area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis, and compared with available nomograms and PET/CT visual evaluation result.

RESULTS: Our multimodal model incorporated clinical information, maximum standardized uptake value (SUVmax), and PET/CT deep learning features. The AUC for predicting LNI was 0.89 (95% confidence interval [CI] 0.81-0.97) for the final model. The proposed model demonstrated superior predictive accuracy in the test cohort compared to PET/CT visual evaluation result, the Memorial Sloan Kettering Cancer Center (MSKCC) and the Briganti-2017 nomograms (AUC 0.85 [95% CI 0.69-1.00] vs. 0.80 [95% CI 0.64-0.95] vs. 0.79 [95% CI 0.61-0.97] and 0.69 [95% CI 0.50-0.88], respectively). The proposed model showed similar calibration and higher net benefit as compared to the traditional nomograms.

CONCLUSION: Our multimodal deep learning model, which incorporates preoperative PSMA PET/CT imaging, shows enhanced predictive capabilities for LNI in clinically localized PCa compared to PSMA PET/CT visual evaluation result and existing nomograms like the MSKCC and Briganti-2017 nomograms. This model has the potential to reduce unnecessary ePLND procedures while minimizing the risk of missing cases of LNI.

PMID:39865180 | DOI:10.1007/s00259-024-07065-2