PLoS One. 2025 Apr 24;20(4):e0320979. doi: 10.1371/journal.pone.0320979. eCollection 2025.

ABSTRACT

The agricultural economy heavily relies on poultry production, making accurate forecasting of poultry data crucial for optimizing revenue, streamlining resource utilization, and maximizing productivity. This research introduces a novel application of the N-BEATS architecture for multi-dimensional poultry data forecasting with enhanced interpretability through an integrated Explainable AI (XAI) framework. Leveraging its advanced capabilities in time series modeling, N-BEATS is applied to predict multiple facets of poultry disease diagnostics using a multivariate dataset comprising key environmental parameters. The methodology empowers decision-making in poultry farm management by providing transparent and interpretable forecasts. Experimental results demonstrate that N-BEATS outperforms conventional deep learning models, including LSTM, GRU, RNN, and CNN, across various error metrics, achieving MAE of 0.172, RMSE of 0.313, MSLE of 0.042, R-squared of 0.034, and RMSLE of 0.204. The positive R-squared value indicates the model's robustness against underfitting and overfitting, surpassing the performance of other models with negative R-squared values. This study establishes N-BEATS as a superior and interpretable solution for complex, multi-dimensional forecasting challenges in poultry production, with significant implications for enhancing predictive analytics in agriculture.

PMID:40273069 | DOI:10.1371/journal.pone.0320979

PLOS Glob Public Health. 2025 Apr 24;5(4):e0004018. doi: 10.1371/journal.pgph.0004018. eCollection 2025.

ABSTRACT

Nomadic pastoralists are systematically underrepresented in the planning of health services and frequently missed by health campaigns due to their mobility. Previous studies have developed novel geospatial methods to address these challenges but rely on manual techniques that are too time and resource-intensive to scale on a national or regional level. To address this gap, we developed a computer vision-based approach to automatically locate active nomadic pastoralist settlements from satellite imagery. We curated labeled datasets of satellite images capturing approximately 1,000 historically active settlements in the Omo Valley of Ethiopia and the Samburu County of Kenya to train and evaluate deep learning models, studying their robustness to low spatial resolutions and limits in labeled training data. Using a novel training strategy that leveraged public road and water infrastructure data, we closed performance gaps introduced by shortages in labeled settlement data. We deployed our best model on a region spanning 5,400 square kilometers in the Omo Valley of Ethiopia, resulting in the identification of historical settlements with a 270-fold reduction in manual review volume. Our work serves as a promising framework for automating the localization of nomadic pastoralist settlements at a national scale for health campaigns and demographic surveillance.

PMID:40273062 | DOI:10.1371/journal.pgph.0004018

PLoS One. 2025 Apr 24;20(4):e0320566. doi: 10.1371/journal.pone.0320566. eCollection 2025.

ABSTRACT

This study proposes an advanced elevator fault precursor prediction method integrating Variational Mode Decomposition (VMD), Bidirectional Long Short-Term Memory (BILSTM), and an Autoencoder with an Attention Mechanism (AEAM), collectively referred to as the VMD-BILSTM-AEAM algorithm. This model addresses the challenges of feature redundancy and noise interference in elevator operation data, improving the stability and accuracy of fault predictions. Using a dataset of elevator operation parameters, including current, voltage, and running speed, the model utilizes the Attribute Correlation Density Ranking (ACDR) method for feature selection and the TSO-optimized VMD for denoising, enhancing data quality. Cross-validation and statistical analyses, including confidence interval calculations, were employed to validate the robustness of the model. The results demonstrate that the VMD-BILSTM-AEAM algorithm achieves a mean True Positive Rate (TPR) of 0.919 with a 95% confidence interval of 0.915 to 0.924, a mean False Positive Rate (FPR) of 0.090 with a 95% confidence interval of 0.087 to 0.092, and a mean Area Under the Curve (AUC) of 0.919 with a 95% confidence interval of 0.915 to 0.923. These performance metrics indicate a significant improvement over traditional and other deep learning models, confirming the model's superiority in predictive maintenance of elevators. The robust capability of the VMD-BILSTM-AEAM algorithm to accurately process and analyze time-series data, even in the presence of noise, highlights its potential for broader applications in predictive maintenance and fault detection across various domains.

PMID:40273057 | DOI:10.1371/journal.pone.0320566

PLoS One. 2025 Apr 24;20(4):e0320242. doi: 10.1371/journal.pone.0320242. eCollection 2025.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a fatal outcome, known for its rapid progression and unpredictable clinical course. However, the tools available for diagnosing and treating IPF are quite limited. This study aims to identify and screen potential biomarkers for IPF diagnosis, thereby providing new diagnostic approaches.

METHODS: We choosed datasets from the Gene Expression Omnibus (GEO) database, including samples from both IPF patients and healthy controls. For the training set, we combined two gene array datasets (GSE24206 and GSE10667) and utilized GSE32537 as the test set. We identified differentially expressed genes (DEGs) between IPF and normal tissues and determined IPF-related modules using Weighted Gene Co-expression Network Analysis (WGCNA). Subsequently, we employed two machine learning strategies to screen potential diagnostic biomarkers. Candidate biomarkers were quantitatively evaluated using Receiver Operating Characteristic (ROC) curves to identify key diagnostic genes, followed by the construction of a nomogram. Further validation of the expression of these genes through transcriptomic sequencing data from IPF and normal group animal models. Next, we conducted immune infiltration analysis, single-gene Gene Set Enrichment Analysis (GSEA), and targeted drug prediction. Finally, we created an artificial neural network model specifically for IPF.

RESULTS: We identified ASPN, COMP, and GPX8 as candidate biomarker genes for IPF, all of which exhibited Area Under the Curve (AUC) above 0.90. These genes were validated by RT-qPCR. Immune infiltration analysis revealed that specific immune cell types are closely related to IPF, suggesting that these immune cells may play a significant role in the pathogenesis of IPF.

CONCLUSION: ASPN, COMP, and GPX8 have been identified as potential diagnostic genes for IPF, and the most relevant immune cell types have been determined. Our research results propose potential biomarkers for diagnosing IPF and present new pathways for investigating its pathogenesis and devising novel therapeutic approaches.

PMID:40273141 | DOI:10.1371/journal.pone.0320242

Rheumatology (Oxford). 2025 Apr 24:keaf218. doi: 10.1093/rheumatology/keaf218. Online ahead of print.

ABSTRACT

OBJECTIVES: This randomized, placebo-controlled pilot trial evaluated the efficacy and safety of abatacept in patients with anti-synthetase syndrome-associated interstitial lung disease (ASyS-ILD).

METHODS: Participants with active ASyS-ILD were randomized to receive abatacept (n = 9) or placebo (n = 11) for 24 weeks, followed by a 24-week open-label extension with abatacept for all participants. The primary end point was a change in % predicted forced vital capacity (%FVC) from baseline to week 24. Secondary endpoints included changes in the FVC (ml), % predicted diffusing capacity for carbon monoxide (%DLCO), shortness of breath questionnaire (SOBQ), and pulmonary disease activity on a visual analogue scale (VAS) at weeks 24 and 48. Pre-post baseline analysis of FVC and quantitative image analysis (QIA) of high-resolution computed tomographic scans were performed. Data was analyzed using a generalized linear mixed model. The study was not powered for primary or secondary endpoints.

RESULTS: At week 24, there was no significant difference in the primary end point of %FVC change between abatacept and placebo (between treatment difference of -0.35, 95%CI -6.91-6.21, p= 0.914) and in all secondary endpoints. However, by week 48, trends favoring abatacept in %FVC, FVC (ml), %DLCO, and SOBQ were observed without statistical significance. There was a significant improvement in pulmonary disease activity VAS and pre-post baseline slopes of %FVC and QIA scores in the abatacept arm. Abatacept was generally well tolerated.

CONCLUSION: Abatacept did not significantly improve %FVC at 24 weeks. However, trends at 48 weeks suggest potential benefits, supporting the need for a larger, long-term randomized controlled trial.

CLINICAL TRIAL REGISTRATION: clinicaltrials.gov; NCT03215927.

PMID:40272902 | DOI:10.1093/rheumatology/keaf218

Neurology. 2025 May 27;104(10):e213592. doi: 10.1212/WNL.0000000000213592. Epub 2025 Apr 24.

ABSTRACT

BACKGROUND AND OBJECTIVES: Coffee intake is linked to a reduced risk of Parkinson disease (PD), but whether this effect is mediated by gut microbiota and metabolomic changes remains unclear. This study examines PD-associated metabolomic shifts, caffeine metabolism, and their connection to gut microbiome alterations in a multicenter study.

METHODS: We conducted an untargeted serum metabolomic assay using liquid chromatography with high-resolution mass spectrometry on an exploratory cohort recruited from National Taiwan University Hospital (NTUH). A targeted metabolomic assay focusing on caffeine and its 12 downstream metabolites was conducted and validated in an independent cohort from University Malaya Medical Centre (UMMC). In the exploratory cohort, the association of each caffeine metabolite with gut microbiota changes was investigated by metagenomic shotgun sequencing. A clustering-based approach was used to correlate microbiome changes with plasma caffeine metabolite level and clinical severity. Body mass index, antiparkinsonism medication use, and dietary habits (including coffee and tea intake) were recorded.

RESULTS: Sixty-three patients with PD and 54 controls from NTUH formed the exploratory cohort while 36 patients with PD and 20 controls from UMMC served as an validation cohort to replicate the plasma caffeine findings. A total of 5,158 metabolites were detected from untargeted metabolomic analysis, with 3,131 having high confidence for analysis. Compared with controls, the abundance of 56 metabolites was significantly higher and that of 7 metabolites was significantly lower (adjusted p < 0.05 and log2 fold change >1) in patients with PD. Caffeine metabolism was significantly lower in patients with PD (p = 0.0013), and serum levels of caffeine and its metabolites negatively correlated with motor severity (p < 0.01). Targeted metabolomic analysis confirmed reduced levels of caffeine and its metabolites, including theophylline, paraxanthine, 1,7-dimethyluric acid, and 5-acetylamino-6-amino-3-methyluracil, in patients with PD; these findings were replicated in the validation cohort (p < 0.05). A clustering approach found that 56 microbiome species enriched in patients with PD negatively correlated with caffeine and its metabolites paraxanthine and theophylline (both p < 0.05), notably Clostridium sp000435655, Acetatifactor sp900066565, Oliverpabstia intestinalis, and Ruminiclostridium siraeum.

DISCUSSION: This study identifies PD-related changes in microbial-caffeine metabolism compared with controls. Our findings offer insights for future functional research on caffeine-microbiome interactions in PD.

PMID:40273394 | DOI:10.1212/WNL.0000000000213592

ACS Appl Bio Mater. 2025 Apr 24. doi: 10.1021/acsabm.5c00307. Online ahead of print.

ABSTRACT

In public health emergencies or in resource-constrained settings, laboratory-based diagnostic methods, such as RT-qPCR, need to be complemented with accurate, rapid, and accessible approaches to increase testing capacity, as this will translate into better outcomes in disease prevention and management. Here, we develop an original nucleic acid detection platform by leveraging CRISPR-Cas9 and lateral flow immunochromatography technologies. In combination with an isothermal amplification that runs with a biotinylated primer, the system exploits the interaction between the CRISPR-Cas9 R-loop formed upon targeting a specific nucleic acid and a fluorescein-labeled probe to generate a visual readout on a lateral flow device. Our method enables rapid, sensitive detection of nucleic acids, achieving a limit of 1-10 copies/μL in 1 h at a low temperature. We validated the efficacy of the method by using clinical samples of patients infected with SARS-CoV-2. Compared with other assays, it operates with more accessible molecular elements and showcases a robust signal-to-noise ratio. Moreover, multiplexed detection was demonstrated using primers labeled with biotin and digoxigenin, achieving the simultaneous identification of target genes on lateral flow devices with two test lines. We successfully detected SARS-CoV-2 and Influenza A (H1N1) in spiked samples, highlighting the potential of the method for multiplexed diagnostics of respiratory viruses. All in all, this represents a versatile and manageable platform for point-of-care testing, thereby supporting better patient outcomes and enhanced pandemic preparedness.

PMID:40273314 | DOI:10.1021/acsabm.5c00307

Front Med (Lausanne). 2025 Apr 9;12:1576375. doi: 10.3389/fmed.2025.1576375. eCollection 2025.

ABSTRACT

Myocardial Infarction (MI) and lung cancers are major contributors to mortality worldwide. While seemingly diverse, the two share common risk factors, such as smoking and hypertension. There is a pressing need to identify bidirectional molecular signatures that link MI and lung cancer, in order to improve clinical outcomes for patients. In this study, we identified common differentially expressed genes between MI and lung cancer. Specifically, we identified 1,496 upregulated and 1,482 downregulated genes in the MI datasets. By focusing on the 1,000 most upregulated and downregulated genes in Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC), we identified 35 genes that are common across MI, LUAD, and LUSC. Functional enrichment analysis revealed shared biological processes, such as "inflammatory response" and "cell differentiation." The Cox proportional hazards model demonstrated a significant association between the shared genes and overall survival in lung cancer patients, as well as with smoking history in these patients. In addition, a machine learning model based on the expression of the shared genes distinguished MI patients from controls, achieving an AUROC of 0.72 and an AUPRC of 0.86. Finally, based on drug repurposing analysis, we proposed FDA-approved drugs potentially targeting the upregulated genes as novel therapeutic options for the co-occurring conditions of MI and lung cancer. Overall, our findings highlight the similarities in molecular makeup between lung cancer and MI.

PMID:40270498 | PMC:PMC12014433 | DOI:10.3389/fmed.2025.1576375

Indian J Ophthalmol. 2025 May 1;73(5):622-636. doi: 10.4103/IJO.IJO_1542_24. Epub 2025 Apr 24.

ABSTRACT

Rare pediatric retinal disorders present significant challenges in diagnosis and management due to their limited prevalence and diverse clinical manifestations. This paper provides a comprehensive review of select rare retinal disorders affecting the pediatric population, focussing a brief on their epidemiology, clinical characteristics, diagnostic modalities, and therapeutic interventions. Through a systematic examination of current literature and clinical case studies, this review aims to elucidate the distinct features and challenges associated with each disorder. Despite the rarity of these conditions, their impact on visual function and quality of life necessitates heightened awareness among clinicians and researchers to facilitate timely diagnosis, appropriate management, and improved outcomes for affected children as their visual systems are still developing. Furthermore, advancements in diagnostic modalities such as fundus fluorescein angiography, optical coherence tomography, electroretinography, and genetic testing are examined for their role in enhancing our understanding of rare pediatric retinal disorders and facilitating early intervention strategies. The literature selection for this article was conducted through PubMed, Google Scholar, and the Cochrane Library databases. A thorough systematic search was carried out for the concerned diseases. Relevant review articles, original research studies, case series, and reports were examined. Additionally, references from these sources were reviewed and included if they provided pertinent information on the topic. The search was not restricted by publication date.

PMID:40272290 | DOI:10.4103/IJO.IJO_1542_24

Orphanet J Rare Dis. 2025 Apr 23;20(1):193. doi: 10.1186/s13023-025-03691-7.

ABSTRACT

BACKGROUND: The socioeconomic impact of rare diseases has been mostly studied at the macrolevel, but evidence at the microlevel is lacking, which overshadows health-related social inequalities affecting people with rare diseases, namely, health selection effects.

AIM: This study presents an overview of employment and work ability in individuals living with rare diseases, two factors related to health selection effects.

METHODS: A systematic literature review was conducted using the PRISMA checklist. Three electronic databases, PubMed, Embase, and Web of Science, were searched from 2013 to 2023. Eligible studies needed to investigate at least one work-related outcome measuring employment or work ability in individuals living with rare diseases and to compare it with a control group. Indeed, including only studies with matched or standardized control groups is essential for ensuring the reliability and validity of research findings.

RESULTS: Of the 7,694 abstracts identified, 44 studies, including 34 rare diseases, met the inclusion criteria. Administrative databases were used to collect work-related data in 48% of the studies, and 73% of the studies employed matching methods for comparison. Overall, 52% of the studies focused solely on employment, 14% focused solely on work ability and 34% included both categories. Individuals with rare diseases were less likely to be employed or more likely to be unemployed than controls in 68% of the studies and 87% of the studies reported that individuals with rare diseases were more likely to be work disabled. Regarding work ability, 90% of the studies reported more missed work time in cases than in controls, and more perceived impairment at work was found in 100% of the studies.

DISCUSSION/CONCLUSION: These results show that individuals with rare diseases tend to have poor work outcomes, but methodological limitations hamper the understanding of health selection effects. Implications for future research and policy-making are discussed.

PMID:40270029 | DOI:10.1186/s13023-025-03691-7

Immunotherapy. 2025 Apr 24:1-8. doi: 10.1080/1750743X.2025.2493038. Online ahead of print.

ABSTRACT

PURPOSE: While CD33 directed immunotherapies have caught significant interest in recent years, the only approved antibody-drug conjugate targeting this antigen for AML is gemtuzumab ozogamicin, which targets the IgV-domain of CD33. Unfortunately, in its current form, these are not effective in a significant proportion of patients due to the presence of a splicing SNP resulting in the loss of IgV-domain. This, however, can be mitigated by targeting the IgC2-domain of CD33; thus, this study aimed to characterize CD33-D2 isoform using the recently developed CD33-D2-targeting antibody HL2541.

METHODS: Genetically engineered AML cell lines expressing CD33 isoforms were tested for antibody-bound internalization and response to GO in vitro. AML-bearing NSG-SGM3 mice were used to evaluate CD33-D2 localization and targeting by the HL2541 antibody in vivo.

RESULTS: HL2541-bound-CD33-D2 is internalized similar to CD33-FL upon binding the antibody component of GO. Co-existence of both isoforms compromises the internalization by >2.5-3-fold for each isoform in the AML cell lines, further resulting in 7-9.5-fold higher IC50 values compared to cells expressing only CD33-FL. Finally, we demonstrate that AML cells expressing CD33-D2 localize to bones in mice and are targeted by HL2541antibody in vivo.

CONCLUSION: The results establish the relevance of targeting IgC domain as an alternative immunotarget to supplement AML chemotherapy.

PMID:40272002 | DOI:10.1080/1750743X.2025.2493038

Front Psychiatry. 2025 Apr 9;16:1587875. doi: 10.3389/fpsyt.2025.1587875. eCollection 2025.

ABSTRACT

Depression is a complex and heterogeneous mental health disorder affecting an estimated 280 million individuals worldwide. Although various antidepressant medications are available, a significant proportion of patients experience medication-resistant depression. This clinical case report highlights the critical importance of integrating pharmacogenomics into clinical practice, which is still not routinely done in many countries, through the detailed examination of a mid-20s male patient diagnosed with medication-resistant depression. Genetic analysis revealed specific variations in the cytochrome P450 genes, namely CYP2D6, CYP2C19, and CYP1A2, which are crucial for drug metabolism. By investigating the impact of these genetic variations on the patient's treatment response, we provide evidence-based recommendations for adjusting antidepressant medications based on the individual's unique pharmacogenomic profile. As demonstrated in the case report, this ultimately results in a positive clinical outcome and would have been advantageous to implement earlier as part of the patient's management.

PMID:40270569 | PMC:PMC12014734 | DOI:10.3389/fpsyt.2025.1587875

Diabetes. 2025 Apr 24:db250039. doi: 10.2337/db25-0039. Online ahead of print.

ABSTRACT

This year marks the 75th anniversary of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health. NIDDK's long history of research and innovation includes support of four types of collaborative research centers focused on diabetes, endocrinology, and metabolic diseases. The Diabetes Research Centers promote basic and clinical diabetes research, while the Centers for Diabetes Translation Research conduct diabetes research across the translation science spectrum. The Mouse Metabolic Phenotyping Center (MMPC)-Live program provides the research community with standardized phenotyping services for mouse models of diabetes and obesity, and the Cystic Fibrosis Research and Translation Centers advance basic, preclinical, and clinical research for cystic fibrosis. These centers have evolved over time in response to new scientific opportunities and to expand their reach to be an asset to the larger scientific community. Looking to the future, NIDDK will continue to ensure that these centers enhance the research community, foster novel and synergistic scientific collaborations, and promote career development of scientists in the early stages of their careers. We will also ensure that our centers align with NIDDK's goal of improving health outcomes for all people with and at risk for diseases, within our mission.

ARTICLE HIGHLIGHTS: NIDDK's research centers focused on diabetes, endocrinology, and metabolism serve broad communities of investigators and address existing research gaps to propel scientific progress. The Diabetes Research Centers support basic and clinical research, and the Centers for Diabetes Translation Research support research across the translation science spectrum. The MMPC-Live program provides phenotyping services for mouse models of diabetes and obesity, and the Cystic Fibrosis Research and Translation Centers advance basic, preclinical, and clinical research for cystic fibrosis. Future goals for the centers include fostering novel and synergistic scientific collaborations, as well as continuing to promote career development.

PMID:40272256 | DOI:10.2337/db25-0039

Elife. 2025 Apr 24;13:RP98851. doi: 10.7554/eLife.98851.

ABSTRACT

Pseudomonas aeruginosa (PA) is an opportunistic, frequently multidrug-resistant pathogen that can cause severe infections in hospitalized patients. Antibodies against the PA virulence factor, PcrV, protect from death and disease in a variety of animal models. However, clinical trials of PcrV-binding antibody-based products have thus far failed to demonstrate benefit. Prior candidates were derivations of antibodies identified using protein-immunized animal systems and required extensive engineering to optimize binding and/or reduce immunogenicity. Of note, PA infections are common in people with cystic fibrosis (pwCF), who are generally believed to mount normal adaptive immune responses. Here, we utilized a tetramer reagent to detect and isolate PcrV-specific B cells in pwCF and, via single-cell sorting and paired-chain sequencing, identified the B cell receptor (BCR) variable region sequences that confer PcrV-specificity. We derived multiple high affinity anti-PcrV monoclonal antibodies (mAbs) from PcrV-specific B cells across three donors, including mAbs that exhibit potent anti-PA activity in a murine pneumonia model. This robust strategy for mAb discovery expands what is known about PA-specific B cells in pwCF and yields novel mAbs with potential for future clinical use.

PMID:40272253 | DOI:10.7554/eLife.98851

Virulence. 2025 Dec;16(1):2494039. doi: 10.1080/21505594.2025.2494039. Epub 2025 Apr 24.

ABSTRACT

Multispecies biofilms are communities composed of different microorganisms embedded in an auto-synthesized polymeric matrix. Pseudomonas aeruginosa and Burkholderia cenocepacia are two multidrug-resistant and biofilm-forming opportunistic pathogens often found in the lungs of people living with cystic fibrosis. In this context, planktonic, static, and dynamic biofilms and in vivo models of both species were optimized in this work to understand their population dynamics, disposition, virulence, and antibiotic susceptibility. From the coculture models optimized in this work, we determined that B. cenocepacia grows in a clustered, aggregative manner at the bottom layers of biofilms, in close contact with P. aeruginosa, that tends to occupy the top layers. Their coexistence increases virulence-related gene expression in both species at early stages of coinfection and in in vivo models, while there was a general downregulation of virulence-related genes after longer coexistence periods as they eventually reach a non-competitive stage during chronic infections. When evaluating antimicrobial susceptibility, a decrease of antimicrobial tolerance was observed in both species when co-cultured. These findings shed light on the differential behavior of P. aeruginosa and B. cenocepacia in dual-species systems, stressing the relevance of multispecies studies in the clinical context.

PMID:40272017 | DOI:10.1080/21505594.2025.2494039

J Chem Inf Model. 2025 Apr 24. doi: 10.1021/acs.jcim.4c01932. Online ahead of print.

ABSTRACT

Cystic fibrosis (CF) is an autosomal recessive genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel. The first nucleotide-binding domain (NBD1) of the CFTR is considered to be a hotspot for CF-causing mutations, and some of these mutations compromise the domain's thermal stability as well as its interactions with other domains. The mechanisms by which such mutations exert their deleterious effects are important in the basic research of this complex disease as well as for the development of mutation-specific therapies. With this in mind, we studied two class-II, severe, CF-causing mutations, L467P and A559T, known to destabilize the domain by 19.3 and 10.7 °C, respectively, and to lead to a misfolded, nonfunctioning CFTR, by conducting microsecond-long molecular dynamics (MD) simulations at an elevated temperature of 410 K on L467P-NBD1 and A559T-NBD1 constructs. For comparison, similar simulations were also performed on the wild-type (WT) construct and on the 6SS-NBD1 and 2PT/M470V-NBD1 constructs, both bearing sets of stabilizing mutations that stabilize the domain by 17.5 and 8.2 °C, respectively. The resulting trajectories were analyzed using multiple metrics, leading to a good correlation between the experimental ΔTm values and the results of the simulations, as well as multiple experimental observations and results of previous modeling efforts. Specifically, our analyses point to specific regions within NBD1 that are substantially affected by the L467P and A559T mutations and, therefore, may play some role in their pathogenesis. Many of these regions are also known to be important for the proper folding and function of the full-length CFTR. Using time-dependent assignment of DSSP elements, we also found that the two mutants follow different disintegration pathways, that of L467P-NBD1 starting in region 464-471 which resides within the F1-like ATP-binding core subdomain and continues in regions 550-562 and 514-523 within the ABCα subdomain whereas that of A559T-NBD1 simultaneously starting at the 550-562 and 514-523 regions. We propose that the analyses presented in this work may pave the way toward the development of L467P and A559T-specific CF therapies and by extension to other mutation-specific therapies for CF and for other diseases involving mutations in NBDs of other proteins.

PMID:40271665 | DOI:10.1021/acs.jcim.4c01932

Dis Colon Rectum. 2025 Mar 4. doi: 10.1097/DCR.0000000000003677. Online ahead of print.

ABSTRACT

BACKGROUND: Obesity is a risk factor for diverticulitis. However, it remains unclear whether visceral fat area, a more precise measurement of abdominal fat, is associated with the risk of diverticulitis.

OBJECTIVE: To estimate the risk of incident and recurrent diverticulitis according to visceral fat area.

DESIGN: A retrospective cohort study.

SETTINGS: The Mass General Brigham Biobank.

PATIENTS: 6,654 patients who underwent abdominal CT for clinical indications and had no diagnosis of diverticulitis, inflammatory bowel disease, or cancer before the scan.

MAIN OUTCOME MEASURES: Visceral fat area, subcutaneous fat area, and skeletal muscle area were quantified using a deep-learning model applied to abdominal CT. The main exposures were z-scores of body composition metrics, normalized by age, sex, and race. Diverticulitis cases were defined with the ICD codes for the primary or admitting diagnosis from the electronic health records. The risks of incident diverticulitis, complicated diverticulitis, and recurrent diverticulitis requiring hospitalization according to quartiles of body composition metrics z-scores were estimated.

RESULTS: A higher visceral fat area z-score was associated with an increased risk of incident diverticulitis (multivariable HR comparing the highest versus lowest quartile, 2.09; 95% CI, 1.48-2.95; P for trend <.0001), complicated diverticulitis (HR, 2.56; 95% CI, 1.10-5.99; P for trend = .02), and recurrence requiring hospitalization (HR, 2.76; 95% CI, 1.15-6.62; P for trend = .03). The association between visceral fat area and diverticulitis was not materially different among different strata of body mass index. Subcutaneous fat area and skeletal muscle area were not significantly associated with diverticulitis.

LIMITATIONS: The study population was limited to individuals who underwent CT scans for medical indication.

CONCLUSION: Higher visceral fat area derived from CT was associated with incident and recurrent diverticulitis. Our findings provide insight into the underlying pathophysiology of diverticulitis and may have implications for preventive strategies. See Video Abstract.

PMID:40272787 | DOI:10.1097/DCR.0000000000003677

Int J Comput Assist Radiol Surg. 2025 Apr 24. doi: 10.1007/s11548-025-03359-4. Online ahead of print.

ABSTRACT

PURPOSE: Cerebral digital subtraction angiography (DSA) is a standard imaging technique in image-guided interventions for visualizing cerebral blood flow and therapeutic guidance thanks to its high spatio-temporal resolution. To date, cerebral perfusion characteristics in DSA are primarily assessed visually by interventionists, which is time-consuming, error-prone, and subjective. To facilitate fast and reproducible assessment of cerebral perfusion, this work aims to develop and validate a fully automatic and quantitative framework for perfusion DSA.

METHODS: We put forward a framework, perfDSA, that automatically generates deconvolution-based perfusion parametric images from cerebral DSA. It automatically extracts the arterial input function from the supraclinoid internal carotid artery (ICA) and computes deconvolution-based perfusion parametric images including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and Tmax.

RESULTS: On a DSA dataset with 1006 patients from the multicenter MR CLEAN registry, the proposed perfDSA achieves a Dice of 0.73(±0.21) in segmenting the supraclinoid ICA, resulting in high accuracy of arterial input function (AIF) curves similar to manual extraction. Moreover, some extracted perfusion images show statistically significant associations (P=2.62e - 5) with favorable functional outcomes in stroke patients.

CONCLUSION: The proposed perfDSA framework promises to aid therapeutic decision-making in cerebrovascular interventions and facilitate discoveries of novel quantitative biomarkers in clinical practice. The code is available at https://github.com/RuishengSu/perfDSA .

PMID:40272658 | DOI:10.1007/s11548-025-03359-4

Immunol Res. 2025 Apr 24;73(1):76. doi: 10.1007/s12026-025-09632-7.

ABSTRACT

Artificial intelligence (AI) has revolutionized various biomedical fields, particularly immunology, by enhancing vaccine development, immunotherapies, and allergy treatments. AI helps identify potential vaccine candidates and predict how the body reacts to different antigens based on a vast number of genomic sequences and protein structures. AI can help cancer patients by analyzing their data and offering personalized immunotherapies. AI has also advanced the field of allergy by identifying potential allergens and predicting allergic reactions based on patient genetic and environmental factors. AI could also help diagnose multiple immunological diseases, including autoimmune diseases and immunodeficiencies, by analyzing patient history and laboratory results. AI has deepened our understanding of the human genome by providing numerous amounts of data from DNA sequences previously believed to be nonfunctional. Through machine learning and deep learning, many laborious research tasks, such as screening for DNA mutations, can be efficiently performed in a short amount of time. AI and machine learning are significantly advancing biomedical science in significant areas, including research and industry. This review discusses the latest AI-based tools that can be utilized in the field of immunology. AI tools significantly advance the field of medical research and healthcare by enabling new scientific discoveries and facilitating rapid clinical diagnosis.

PMID:40272607 | DOI:10.1007/s12026-025-09632-7

Invest Ophthalmol Vis Sci. 2025 Apr 1;66(4):72. doi: 10.1167/iovs.66.4.72.

ABSTRACT

PURPOSE: To develop a deep learning method for vessel segmentation in fundus images, measure retinal vessels, and study the connection between retinal vascular features and systemic indicators in diabetic patients.

METHODS: We conducted a study on patients with diabetes mellitus (DM) at various stages of diabetic retinopathy (DR) using data from the Joint Asia Diabetes Evaluation (JADE) Register. All participants underwent comprehensive clinical assessments, including anthropometric measurements, laboratory tests, and fundus photography, during each follow-up visit (2.81 average follow-up visits). A custom U-Net deep learning model utilizing a variety of open-source datasets was developed for the segmentation and measurement of retinal vessels. We investigated the relationship between systemic indicators and the severity of DR, analyzing the correlation coefficients between systemic indicators and retinal vascular characteristics.

RESULTS: We enrolled a total of 637 patients diagnosed with DM and collected 3575 series of photographs for analysis. Some of the systemic indicators and retinal vascular metrics, including central retinal arteriolar equivalent, central retinal venular equivalent, arteriole-to-venule ratio, and fractal dimension, were significantly correlated with the severity of diabetic retinopathy (P < 0.05). Some physical characteristics, hematological parameters, renal function parameters, metabolism-related parameters, biochemical markers such as folic acid and fasting insulin, liver enzymes, and macrovascular indicators were significantly correlated with certain retinal vascular metrics (P < 0.05).

CONCLUSIONS: Multiple systemic indicators were identified as significantly associated with the advancement of diabetic retinopathy and retinal vascular metrics. Utilizing deep learning techniques for vessel segmentation and measurement on color fundus photographs can help elucidate the connections between retinal vascular characteristics and systemic indicators.

PMID:40272369 | DOI:10.1167/iovs.66.4.72