PLoS Comput Biol. 2025 Apr 23;21(4):e1013011. doi: 10.1371/journal.pcbi.1013011. Online ahead of print.

ABSTRACT

Animal acoustic communication contains many structural features. Among these, temporal structure, or rhythmicity, is increasingly tested empirically and modelled quantitatively. Accelerando is a rhythmic structure which consists of temporal intervals increasing in rate over a sequence. Why this particular vocal behaviour is widespread in many different animal lineages, and how it evolved, is so far unknown. Here, we use evolutionary game theory and computer simulations to link two rhythmic aspects of animal communication, synchronization and overlap: We test whether rhythmic accelerando could evolve under a pressure for acoustic overlap in time. Our models show that higher acceleration values result in a higher payoff, driven by the higher relative overlap between sequences. The addition of a cost to the payoff matrix models a physiological disadvantage to high acceleration rates and introduces a divergence between an individual's incentive and the overall payoff of the population. Analysis of the invasion dynamics of acceleration strategies shows a stable, non-invadable range of strategies for moderate acceleration levels. Our computational simulations confirm these results: A simple selective pressure to maximise the expected overlap, while minimising the associated physiological cost, causes an initially isochronous population to evolve towards producing increasingly accelerating sequences until a population-wide equilibrium of rhythmic accelerando is reached. These results are robust to a broad range of parameter values. Overall, our analyses show that if overlap is beneficial, emergent evolutionary dynamics allow a population to gradually start producing accelerating sequences and reach a stable state of moderate acceleration. Finally, our modelling results closely match empirical data recorded from an avian species showing rhythmic accelerando, the African penguin. This shows the productive interplay between theoretical and empirical biology.

PMID:40267164 | DOI:10.1371/journal.pcbi.1013011

STAR Protoc. 2025 Apr 22;6(2):103786. doi: 10.1016/j.xpro.2025.103786. Online ahead of print.

ABSTRACT

Crabtree-positive yeasts rapidly consume glucose via glycolysis, making it difficult to experimentally estimate their actual glycolytic rate or flux. We present a stable isotope labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based protocol to quantitatively estimate glycolytic and related carbon metabolic fluxes using Saccharomyces cerevisiae. This approach defines time windows to capture glucose metabolic intermediate production before label saturation, enabling a comparison of glycolytic flux changes across different cells. This protocol provides a reliable, quantitative approach to study dynamic metabolic fluxes in these cells. For complete details on the use and execution of this protocol, please refer to Vengayil et al., 2024.1.

PMID:40266845 | DOI:10.1016/j.xpro.2025.103786

Insects. 2025 Mar 1;16(3):251. doi: 10.3390/insects16030251.

ABSTRACT

In insects, the number of life cycles varies inter- and intra-specifically, and it is widely accepted that the variation in the number of life cycles is an adaptive response to diverse environmental conditions. However, the molecular mechanism that underlies the variety and plasticity in the number of life cycles is largely unknown. In the silkworm, Bombyx mori, the Voltinism (V) locus has three alleles, V1(univoltine; dominant), V2 (bivoltine; standard), and V3 (polyvoltine; recessive), which are known to generate variation in the number of life cycles in a year under natural conditions, with obligatory diapause for the V1 allele, facultative diapause for V2, and non-diapause for V3. Here, we further confirm that the γ-aminobutyric acid (GABA)ergic neuron signal pathway modulates progeny diapause via controlling diapause hormone release. A population genetic analysis (Fst) revealed that the synaptic vesicle glycoprotein 2A and 2B (BmSV2A and BmSV2B) genes, tightly related to the transport of neurotransmitters, are located in the V locus. Importantly, using the CRISPR/Cas9 editing technique, we have discovered that the BmSV2A and BmSV2B genes increased or modified the expression of GABAergic neuron signal pathway genes, respectively. These results demonstrate that BmSV2A and BmSV2B, positioned within the V locus, could be involved in voltinism control via the GABAergic neuron signal pathway.

PMID:40266755 | DOI:10.3390/insects16030251

Med Sci (Basel). 2025 Apr 1;13(2):37. doi: 10.3390/medsci13020037.

ABSTRACT

India's population complexity presents varied challenges in genetic research, and while facilities have gained traction in tier-1 and -2 cities, reliance on international collaborations often delays such investigations. COVID-19 further exacerbated the issues with such sample sharing. Congenital Hyperinsulinism (CHI) is a rare genetic disorder of pancreatic β-cells causing hypoglycaemia in children due to abnormal insulin secretion. Given India's high birth rate and consanguineous populations, annual CHI cases are estimated to be around up to 10,000, with up to 50% having unexplained genetic causes. Diffuse or atypical lesions in such patients often necessitate near-total-pancreatectomy, risking pancreatic exocrine insufficiency and diabetes, requiring lifelong therapy. Also, novel genetic variations complicate accurate diagnosis, risk assessment, and counselling, emphasising the need for rapid genetic assessment to prevent neurological injuries and inform treatment decisions. Despite significant efforts at many institutes, there are no dedicated organisations for CHI in India. With the implementation of the National Policy for Rare Diseases 2021, we plan to form a non-profit organisation, "Congenital Hyperinsulinism India Association (CHIA)", comprising paediatric endocrinologists, paediatricians, geneticists, and independent researchers. The aims of this association are to generate a national database registry of patients, formulate a parent support group and CHIA consortium, design patient information leaflets, as well as foster genomic collaborations and promote clinical trials. Such steps will help sensitise the health authorities and policy makers, urging them to improve the allocation of health budgets for rare diseases, as well as empower patients and their families, contributing towards a better quality of life.

PMID:40265383 | DOI:10.3390/medsci13020037

J Endocrinol Invest. 2025 Apr 23. doi: 10.1007/s40618-025-02586-5. Online ahead of print.

ABSTRACT

Diabetes and cancer are two of the most common public health concerns worldwide. The complex interplay of these two conditions is a growing area of research, as patients with diabetes are at increased risk for developing cancer, and vice versa. Furthermore, both patient populations show increased risk of many communicable infectious diseases and their adverse consequences, while vaccination can play a crucial role in their prevention, improving patient outcomes. Vaccination should represent a standard part of care for patients with cancer, diabetes, and both the diseases simultaneously, including people undergoing cancer treatment or in remission. Several international guidelines provide recommendations for vaccinating people with cancer or diabetes, but the two conditions have not been specifically evaluated together. Here we present a multidisciplinary consensus position paper on vaccination in patients with cancer and diabetes. The position paper is the result of a collaborative effort between experts from the Italian Association of Medical Oncology (AIOM), Italian Association of Medical Diabetologists (AMD), Italian Society of Diabetology (SID), Italian Society of Endocrinology (SIE), and Italian Society of Pharmacology (SIF). The paper provides a comprehensive overview of the current state-of-the-art knowledge on vaccination in patients with cancer and diabetes. It discusses the importance of vaccination in preventing infections, focuses attention on the need to consider the unique challenges faced by patients with cancer and diabetes when it comes to vaccine administration, and highlights the need for coordinated care to optimize treatment outcomes. Overall, the consensus position paper provides healthcare professionals caring for patients with cancer and diabetes recommendations on the use of various vaccines, including influenza, COVID-19, HZV, and HPV vaccines, as well as guidance on how to address common concerns and challenges related to vaccine administration.

PMID:40266540 | DOI:10.1007/s40618-025-02586-5

Cardiol Rev. 2025 Apr 23. doi: 10.1097/CRD.0000000000000937. Online ahead of print.

ABSTRACT

Obesity-related hypertension (HTN) is a growing global health concern, being a significant contributor to cardiovascular morbidity and mortality. The article reviews the complex pathophysiological mechanisms involved in the link between obesity and HTN, including neurohormonal activation, inflammation, insulin resistance, and endothelial dysfunction. The role of adipokines, specifically leptin and adiponectin, in blood pressure regulation is highlighted, along with the impact of advanced glycation end-products on vascular function. We discuss the effectiveness of lifestyle therapies, including weight loss, and diet for the management of obesity HTN. We also discuss the utilization of pharmacologic agents, including GLP-1 receptor agonists, and the impact of bariatric surgery on long-term blood pressure control. Despite enhanced treatment, significant barriers to treatment exist, including obesity stigma, limited access to health care, and adherence problems. Future research must focus on personalized approaches, like pharmacogenomics, to optimize hypertension treatment in the obese.

PMID:40265912 | DOI:10.1097/CRD.0000000000000937

Pediatr Pulmonol. 2025 Apr;60(4):e71097. doi: 10.1002/ppul.71097.

ABSTRACT

BACKGROUND: New 2023 CF liver disease (CFLD) guidelines advocate for additional screening in people with cystic fibrosis (PwCF), including biennial abdominal ultrasound. As a first step towards effective and equitable guidelines implementation, we examined our current practice of CFLD screening and hepatobiliary involvement (HBI) evaluation. We identified characteristics of PwCF at-risk for incomplete screening and factors affecting evaluation.

METHODS: We retrospectively reviewed medical records of PwCF aged 0-21 years, with native liver and ≥ 2 outpatient CF clinic visits 2017-2023. Logistic regression was used to identify characteristics associated with incomplete screening and with HBI.

RESULTS: Amongst 112 PwCF at our center: 37% (n = 42) self-reported as mixed race, 27% (n = 30) as Hispanic; 53% (n = 59) had public insurance. Incomplete lab screening was identified in 19% of our cohort. GGT was the most frequently missed component (14%, n = 16). Hispanics and publicly insured people were more likely to have incomplete screening. Of the 112, 45 met criteria for HBI. Demographics did not predict HBI. Five with CF and HBI had the full hepatitis workup recommended by the new guidelines. Those with HBI documented (42%, n = 19) were more likely to receive additional workup. PwCF who were seen by a gastroenterologist were more likely to have additional diagnostic work-up for HBI.

CONCLUSION: One in five PwCF at our center were incompletely screened for CFLD, with Hispanics and publicly insured at higher risk. Accurate diagnosis and adequate documentation are the first steps to identifying HBI in PwCF. A dedicated CF gastroenterologist is key to completing CFLD screening and liver diagnosis.

PMID:40265529 | DOI:10.1002/ppul.71097

Int J Neonatal Screen. 2025 Apr 17;11(2):27. doi: 10.3390/ijns11020027.

ABSTRACT

Because a delayed diagnosis of cystic fibrosis (CF) is detrimental and may be fatal, screening at birth has become routine in the Western world and has proven beneficial for many reasons, in addition to enabling prompt specialized care. Newborn screening (NBS) programs have elucidated the true incidence of CF in a variety of populations and enabled rapid genotype identification through the analysis of the cystic fibrosis transmembrane regulator (CFTR) gene. NBS studies also have revealed regional and population differences in CFTR variants and refuted the dogma that CF is a "white person's disease". But some regions have not yet implemented CF NBS, particularly in Asia where the disease prevalence has been uncertain. While the needs of a few low-and-middle-income countries are being addressed sequentially, one of the regions of greatest current interest is the Indian subcontinent because of recent data suggesting a higher incidence than that previously assumed, and clinical observations indicating tragic outcomes due to delayed diagnoses or failure to diagnose the disorder in young children. Thus, we conclude that the opportunities for research combined with service in the Indian subcontinent are urgent and potentially very impactful. Consequently, India is the last and best frontier for CF NBS, as we argue herein.

PMID:40265448 | DOI:10.3390/ijns11020027

Int J Neonatal Screen. 2025 Apr 2;11(2):24. doi: 10.3390/ijns11020024.

ABSTRACT

Newborn screening for cystic fibrosis (CF) has been universal in the US since 2010; however, there is significant variation among newborn screening algorithms. Systematic reviews were used to develop seven recommendations for newborn screening program practices to improve timeliness, sensitivity, and equity in diagnosing infants with CF: (1) The CF Foundation recommends the use of a floating immunoreactive trypsinogen (IRT) cutoff over a fixed IRT cutoff; (2) The CF Foundation recommends using a very high IRT referral strategy in CF newborn screening programs whose variant panel does not include all CF-causing variants in CFTR2 or does not have a variant panel that achieves at least 95% sensitivity in all ancestral groups within the state; (3) The CF Foundation recommends that CF newborn screening algorithms should not limit CFTR variant detection to the F508del variant or variants included in the American College of Medical Genetics-23 panel; (4) The CF Foundation recommends that CF newborn screening programs screen for all CF-causing CFTR variants in CFTR2; (5) The CF Foundation recommends conducting CFTR variant screening twice weekly or more frequently as resources allow; (6) The CF Foundation recommends the inclusion of a CFTR sequencing tier following IRT and CFTR variant panel testing to improve the specificity and positive predictive value of CF newborn screening; (7) The CF Foundation recommends that both the primary care provider and the CF specialist be notified of abnormal newborn screening results. Through implementation, it is anticipated that these recommendations will result in improved sensitivity, equity, and timeliness of CF newborn screening, leading to improved health outcomes for all individuals diagnosed with CF following newborn screening and a decreased burden on families.

PMID:40265445 | DOI:10.3390/ijns11020024

Int J Cardiovasc Imaging. 2025 Apr 23. doi: 10.1007/s10554-025-03403-z. Online ahead of print.

ABSTRACT

We hypothesized that deep learning-based post hoc denoising could improve the quality of cardiac CT for the 3D volume-rendered (VR) imaging of mitral valve (MV) prolapse. We aimed to evaluate the quality of denoised 3D VR images for visualizing MV prolapse and assess their diagnostic performance and efficiency. We retrospectively reviewed the cardiac CTs of consecutive patients who underwent MV repair in 2023. The original images were iteratively reconstructed and denoised with a residual dense network. 3DVR images of the "surgeon's view" were created with blood chamber transparency to display the MV leaflets. We compared the 3DVR image quality between the original and denoised images with a 100-point scoring system. Diagnostic confidence for prolapse was evaluated across eight MV segments: A1-3, P1-3, and the anterior and posterior commissures. Surgical findings were used as the reference to assess diagnostic ability with the area under curve (AUC). The interpretation time for the denoised 3DVR images was compared with that for multiplanar reformat images. For fifty patients (median age 64 years, 30 males), denoising the 3DVR images significantly improved their image quality scores from 50 to 76 (P <.001). The AUC in identifying MV prolapse improved from 0.91 (95% CI 0.87-0.95) to 0.94 (95% CI 0.91-0.98) (P =.009). The denoised 3DVR images were interpreted five-times faster than the multiplanar reformat images (P <.001). Deep learning-based denoising enhanced the quality of 3DVR imaging of the MV, improving the performance and efficiency in detecting MV prolapse on cardiac CT.

PMID:40266552 | DOI:10.1007/s10554-025-03403-z

Jpn J Radiol. 2025 Apr 23. doi: 10.1007/s11604-025-01787-5. Online ahead of print.

ABSTRACT

PURPOSE: To investigate whether super-resolution deep learning reconstruction (SR-DLR) of MR myelography-aided evaluations of lumbar spinal stenosis.

MATERIAL AND METHODS: In this retrospective study, lumbar MR myelography of 40 patients (16 males and 24 females; mean age, 59.4 ± 31.8 years) were analyzed. Using the MR imaging data, MR myelography was separately reconstructed via SR-DLR, deep learning reconstruction (DLR), and conventional zero-filling interpolation (ZIP). Three radiologists, blinded to patient background data and MR reconstruction information, independently evaluated the image sets in terms of the following items: the numbers of levels affected by lumbar spinal stenosis; and cauda equina depiction, sharpness, noise, artifacts, and overall image quality.

RESULTS: The median interobserver agreement in terms of the numbers of lumbar spinal stenosis levels were 0.819, 0.735, and 0.729 for SR-DLR, DLR, and ZIP images, respectively. The imaging quality of the cauda equina, and image sharpness, noise, and overall quality on SR-DLR images were significantly better than those on DLR and ZIP images, as rated by all readers (p < 0.001, Wilcoxon signed-rank test). No significant differences were observed for artifacts on SR-DLR against DLR and ZIP.

CONCLUSIONS: SR-DLR improved the image quality of lumbar MR myelographs compared to DLR and ZIP, and was associated with better interobserver agreement during assessment of lumbar spinal stenosis status.

PMID:40266548 | DOI:10.1007/s11604-025-01787-5

Elife. 2025 Apr 23;14:RP101322. doi: 10.7554/eLife.101322.

ABSTRACT

Antibodies are a major component of adaptive immunity against invading pathogens. Here, we explore possibilities for an analytical approach to characterize the antigen-specific antibody repertoire directly from the secreted proteins in convalescent serum. This approach aims to perform simultaneous antibody sequencing and epitope mapping using a combination of single particle cryo-electron microscopy (cryoEM) and bottom-up proteomics techniques based on mass spectrometry (LC-MS/MS). We evaluate the performance of the deep-learning tool ModelAngelo in determining de novo antibody sequences directly from reconstructed 3D volumes of antibody-antigen complexes. We demonstrate that while map quality is a critical bottleneck, it is possible to sequence antibody variable domains from cryoEM reconstructions with accuracies of up to 80-90%. While the rate of errors exceeds the typical levels of somatic hypermutation, we show that the ModelAngelo-derived sequences can be used to assign the used V-genes. This provides a functional guide to assemble de novo peptides from LC-MS/MS data more accurately and improves the tolerance to a background of polyclonal antibody sequences. Following this proof-of-principle, we discuss the feasibility and future directions of this approach to characterize antigen-specific antibody repertoires.

PMID:40266252 | DOI:10.7554/eLife.101322

Radiol Artif Intell. 2025 Apr 23:e240392. doi: 10.1148/ryai.240392. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop and evaluate a novel multitask deep learning framework for automated detection and localization of endoleaks at aortic digital subtraction angiography (DSA) performed during real-world endovascular aneurysm repair (EVAR) procedures for abdominal aortic aneurysm. Materials and Methods This retrospective study analyzed intraoperative aortic DSA images from EVAR patients (January 2017-December 2021). An expert panel assessed each sequence for endoleaks. Each sequence was processed into three input channels: peak density (PD), time to peak (TTP), and area under the time-density curve (AUC-TD), generating three 2D perfusion maps per patient. These maps served as input into a convolutional neural network (CNN) for binary detection (classification) and localization (regression) of endoleaks through multitask learning. Fivefold cross-validation was performed, with patients split 80:20 into training/testing for each fold. Performance metrics included AUC, F1 score, precision, recall and were compared with human experts. Results The study included 220 patients (181 male; median age, 74 years; IQR, 68-79 years). Endoleaks were visible in 111 out of 220 (50.5%) patients. The model identified and localized endoleaks with an AUC of 0.85 (SD 0.0031), F1 score of 0.78 (SD 0.21), 95% precision, and 73% recall. Compared with the procedural team (94% precision, 63% recall), it had higher values in both metrics, with an F1-score within the human observer range (0.75-0.85). Balancing regression and classification by multitask learning delivered optimal results. The interobserver agreement among human experts was moderate (Fleiss' Kappa = 0.404). Conclusion A novel, fully automated deep learning method accurately detected and localized endoleaks on DSA imaging from EVAR procedures. ©RSNA, 2025.

PMID:40266029 | DOI:10.1148/ryai.240392

Epilepsia. 2025 Apr 23. doi: 10.1111/epi.18406. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop and validate a wrist-worn accelerometer-based, deep-learning tunable algorithm for the automated detection of generalized or bilateral convulsive seizures (CSs) to be integrated with off-the-shelf smartwatches.

METHODS: We conducted a prospective multi-center study across eight European epilepsy monitoring units, collecting data from 384 patients undergoing video electroencephalography (vEEG) monitoring with a wrist-worn three dimensional (3D)-accelerometer sensor. We developed an ensemble-based convolutional neural network architecture with tunable sensitivity through quantile-based aggregation. The model, referred to as Episave, used accelerometer amplitude as input. It was trained on data from 37 patients who had 54 CSs and evaluated on an independent dataset comprising 347 patients, including 33 who had 49 CSs.

RESULTS: Cross-validation on the training set showed that optimal performance was obtained with an aggregation quantile of 60, with a 98% sensitivity, and a false alarm rate (FAR) of 1/6 days. Using this quantile on the independent test set, the model achieved a 96% sensitivity (95% confidence interval [CI]: 90%-100%), a FAR of <1/8 days (95% CI: 1/9-1/7 days) with 1 FA/61 nights, and a median detection latency of 26 s. One of the two missed CSs could be explained by the patient's arm, which was wearing the sensor, being trapped in the bed rail. Other quantiles provided up to 100% sensitivity at the cost of a greater FAR (1/2 days) or very low FAR (1/100 days) at the cost of lower sensitivity (86%).

SIGNIFICANCE: This Phase 2 clinical validation study suggests that deep learning techniques applied to single-sensor accelerometer data can achieve high CS detection performance while enabling tunable sensitivity.

PMID:40265999 | DOI:10.1111/epi.18406

Plants (Basel). 2025 Mar 11;14(6):881. doi: 10.3390/plants14060881.

ABSTRACT

Weed competition in inter- and intra-row zones presents a substantial challenge to crop productivity, with intra-row weeds posing a particularly severe threat. Their proximity to crops and higher occlusion rates increase their negative impact on yields. This study examines the efficacy of advanced deep learning architectures-namely, Faster R-CNN, RT-DETR, and YOLOv11-in the accurate identification of weeds and crops within commercial maize fields. A comprehensive dataset was compiled under varied field conditions, focusing on three major weed species: Cyperus rotundus L., Echinochloa crus-galli L., and Solanum nigrum L. YOLOv11 demonstrated superior performance among the evaluated models, achieving a mean average precision (mAP) of 97.5% while operating in real-time at 34 frames per second (FPS). Faster R-CNN and RT-DETR models achieved a mAP of 91.9% and 97.2%, respectively, with processing capabilities of 11 and 27 FPS. Subsequent hardware evaluations identified YOLOv11m as the most viable solution for field deployment, demonstrating high precision with a mAP of 94.4% and lower energy consumption. The findings emphasize the feasibility of employing these advanced models for efficient inter- and intra-row weed management, particularly for early-stage weed detection with minimal crop interference. This study underscores the potential of integrating State-of-the-Art deep learning technologies into agricultural machinery to enhance weed control, reduce operational costs, and promote sustainable farming practices.

PMID:40265804 | DOI:10.3390/plants14060881

Curr Med Imaging. 2025 Apr 22. doi: 10.2174/0115734056370620250403030638. Online ahead of print.

ABSTRACT

INTRODUCTION: To examine the integration of artificial intelligence (AI) into Picture Archiving and Communication Systems (PACS) and assess its impact on medical imaging, diagnostic workflows, and patient outcomes. This review explores the technological evolution, key advancements, and challenges associated with AI-enhanced PACS in healthcare settings.

METHODS: A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science databases, covering articles from January 2000 to October 2024. Search terms included "artificial intelligence," "machine learning," "deep learning," and "PACS," combined with keywords related to diagnostic accuracy and workflow optimization. Articles were selected based on predefined inclusion and exclusion criteria, focusing on peerreviewed studies that discussed AI applications in PACS, innovations in medical imaging, and workflow improvements. A total of 183 studies met the inclusion criteria, comprising original research, systematic reviews, and meta-analyses.

RESULTS: AI integration in PACS has significantly enhanced diagnostic accuracy, achieving improvements of up to 93.2% in some imaging modalities, such as early tumor detection and anomaly identification. Workflow efficiency has been transformed, with diagnostic times reduced by up to 90% for critical conditions like intracranial hemorrhages. Convolutional neural networks (CNNs) have demonstrated exceptional performance in image segmentation, achieving up to 94% accuracy, and in motion artifact correction, further enhancing diagnostic precision. Natural language processing (NLP) tools have expedited radiology workflows, reducing reporting times by 30-50% and improving consistency in report generation. Cloudbased solutions have also improved accessibility, enabling real-time collaboration and remote diagnostics. However, challenges in data privacy, regulatory compliance, and interoperability persist, emphasizing the need for standardized frameworks and robust security protocols. Conclusions The integration of AI into PACS represents a pivotal transformation in medical imaging, offering improved diagnostic workflows and potential for personalized patient care. Addressing existing challenges and enhancing interoperability will be essential for maximizing the benefits of AIpowered PACS in healthcare.

PMID:40265427 | DOI:10.2174/0115734056370620250403030638

Br J Hosp Med (Lond). 2025 Apr 25;86(4):1-22. doi: 10.12968/hmed.2024.0934. Epub 2025 Apr 21.

ABSTRACT

Aims/Background Idiopathic pulmonary fibrosis (IPF) is associated with an increased mortality risk. However, the factors that contribute to this risk remain unknown. This study aimed to systematically review existing predictive models for IPF-related mortality and to evaluate prognostic factors associated with patient outcomes. Methods A comprehensive literature search was conducted on PubMed, Cochrane Library, Web of Science, and Embase for studies on IPF mortality risk prediction models published between 1 January 1984 and 15 November 2024. Two independent reviewers screened, extracted, and cross-checked the data. The risk of bias and model applicability were also evaluated. Results A total of 17 risk prediction models were identified. The area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.728 to 0.907, while the model validation results ranged from 0.750 to 0.920. The concordance index (C-index) of 10 studies was more than 0.7, indicating good predictive performance. This study encompassed a total of 17 risk prediction models incorporating between 3 and 8 combined prognostic variables, with the most frequently included predictors being forced vital capacity as a percentage of the predicted value (FVC%pred), carbon monoxide diffusion capacity as a percentage of the predicted value (DLCO%pred), gender, age, six-minute walk test (6MWT) results, and dyspnea severity. Conclusion Current IPF mortality risk prediction models remain in an exploratory phase, with a generally high risk of bias. Furthermore, the lack of external validation in some models limits their generalizability. Future research should focus on improving the applicability of the model to enhance clinical application.

PMID:40265534 | DOI:10.12968/hmed.2024.0934

Vaccines (Basel). 2025 Feb 28;13(3):259. doi: 10.3390/vaccines13030259.

ABSTRACT

Background/Objectives: Limited research has compared tests assessing humoral and cellular immunity related to SARS-CoV-2 infection. This study evaluated immunoglobulin G for nucleocapsid (IgG(N)) and T-spot for nucleocapsid (T-spot(N)) assays against polymerase chain reaction (PCR) test results for identifying infected individuals. Methods: This study included participants who had completed five blood samplings since their second COVID-19 vaccination between 9 September 2021 and 6 November 2022. Chemiluminescent immunoassay (CLIA) tests measured the humoral immune response, IgG(S) and neutralizing activity tests the immune status, and IgG(N) tests the infection history. For cellar immunity, T-spot(S) indicated immune status, and T-spot(N) indicated infection history. Results: The primary outcome was the proportion of individuals who tested positive for PCR and the proportion who tested positive for IgG(N) and T-spot(N). Overall, this study included 2104 participants. In the PCR-negative group, 1838 individuals tested negative for IgG(N), whereas 64 tested positive at least once. The geometric mean of IgG(S) at T5 was 1541.7 AU/mL in the IgG(N)-negative group and 3965.8 AU/mL in the IgG(N)-positive group, which was 2.6 times higher. In the PCR-positive group, 25 individuals tested negative for IgG(N), while 177 tested positive at least once. The geometric mean of IgG(S) at T5 was 2700.6 AU/mL in the IgG(N)-negative group and 5400.8 AU/mL in the IgG(N)-positive group, showing higher values in the IgG(N)-positive group. Conclusions: A discrepancy was noted between PCR test results and the IgG(N) and T-spot(N) determinations. Combining multiple assays is required to accurately identify the past-infected population.

PMID:40266135 | DOI:10.3390/vaccines13030259

Vaccines (Basel). 2025 Feb 20;13(3):209. doi: 10.3390/vaccines13030209.

ABSTRACT

OBJECTIVES: Determination of reactogenicity and immunogenicity of a pentavalent candidate vaccine against S. flexneri 1b, 2a, 3a, 6, and Y (PLVF).

METHODS: The study involved 80 healthy adult volunteers aged 18-55 years. Groups were subcutaneously immunized twice at a 30-day interval with 62.5 μg/0.5 mL or 125 μg/0.5 mL of the vaccine.

RESULTS: During the entire 8-month period of post-vaccination observation, the vaccine was well tolerated, with no local or systemic reactions detected objectively. The results of laboratory studies demonstrated no effect on the main indicators of hemogram, biochemical blood test, or urinalysis. IgA, IgG, and IgM levels against LPS S. flexneri 1b, 2a, 3a, 6, and Y were examined before vaccination, a month after each vaccination, and 6 months after booster vaccination. One month after vaccination, IgA and IgG seroconversions were observed in 67.5-82.5% (depending on serotype) and 60-77.5% of volunteers, respectively. Booster immunization did not have a significant effect on vaccine immunogenicity. In two separate groups of 15 and 9 volunteers for mucosal sIgA, IgA, and IgG titer determination after immunization with a 125 μg vaccine dose, paired stool, and saliva samples were taken before and one month after vaccination. In 26.7-40% of volunteers, there was a 2-fold and higher increase in sIgA titer for the studied serotypes in the feces and in 66.7-88.9% in saliva. IgA and IgG 2-fold conversion rates were 26.7-53.3% and 33.3-46.7% in the feces, 33.3-77.9%, and 66.7-77.8% in saliva, respectively.

CONCLUSIONS: the tolerability of PLVF and the pronounced humoral immune response allow us to proceed to the phase 3 clinical trial stage.

PMID:40266069 | DOI:10.3390/vaccines13030209

Plants (Basel). 2025 Mar 17;14(6):935. doi: 10.3390/plants14060935.

ABSTRACT

Increasing global temperatures, in tandem with predicted increases in future frequencies of drought and flooding episodes, represent a threat to agricultural productivity [...].

PMID:40265884 | DOI:10.3390/plants14060935