Biomolecules. 2025 Mar 26;15(4):485. doi: 10.3390/biom15040485.

ABSTRACT

Calcium is a ubiquitous second messenger and plays a major role in a variety of cellular functions, both within the same cell and between different cells [...].

PMID:40305225 | DOI:10.3390/biom15040485

Cell Biochem Biophys. 2025 Apr 30. doi: 10.1007/s12013-025-01762-x. Online ahead of print.

ABSTRACT

Acute Respiratory Distress Syndrome (ARDS) is a severe lung disease with a high fatality rate and few treatment options. Targeting certain signalling pathways, notably the Transforming Growth Factor-beta (TGF-beta) signalling pathway, has emerged as a promising option for ARDS therapy. We identified TGF-beta Receptor 1 (TGFBR1) as a major target for ARDS treatment using the STRING and KEGG databases and validated TGFBR1's critical function in the TGF-beta signalling pathway, which is important in ARDS pathogenesis. To find prospective TGFBR1 inhibitors, we selected two FDA-approved medicines, Galunisertib and Vactosertib, which are established pharmacological profiles in cancer and fibrotic illnesses. Furthermore, the SwissSimilarity platform's ligand-based virtual screening revealed structurally related drugs in the DrugBank and ChEMBL databases. Among these, seven candidates were selected for further consideration. Molecular docking experiments found that DB08387 and CHEMBL14297639 had the strongest affinity for TGFBR1, creating strong hydrogen bonds at key sites. These findings point to their potential as TGFBR1 inhibitors in ARDS treatment. The pharmacokinetic screening revealed that most of the chosen compounds had favourable ADME features, with CHEMBL14297639 standing out for its low gastrointestinal absorption and limited cytochrome P450 inhibition. This study demonstrates the possibility of targeting TGFBR1 with Galunisertib, Vactosertib, and other prospective ARDS treatments. The findings lay the groundwork for additional experimental validation and the development of innovative therapeutics aimed at reducing ARDS severity.

PMID:40304856 | DOI:10.1007/s12013-025-01762-x

Clin Transl Sci. 2025 May;18(5):e70237. doi: 10.1111/cts.70237.

ABSTRACT

Clinical trials with investigator sponsors at academic sites have increased, in part due to studies involving drug repurposing, the process of identifying new uses for existing drugs that are initially conducted in patients rather than healthy participants. In contrast to industry- or government-sponsored trials, investigator-sponsored clinical studies, also known as investigator-initiated trials, are typically conducted at one or several academic centers and are resource-limited by finances and patient numbers. These studies can serve as crucial pilot studies to inform the design of larger, more definitive clinical trials. Drawing from the experience of working with clinical researchers in academic settings, this tutorial presents guidelines for writing clinical protocols for resource-limited investigator-sponsored studies that meet international standards and optimize the detection of meaningful signals or outcomes that can lead to investigation in larger well-controlled trials.

PMID:40304394 | DOI:10.1111/cts.70237

J Clin Res Pediatr Endocrinol. 2025 Apr 30. doi: 10.4274/jcrpe.galenos.2025.2024-11-11. Online ahead of print.

ABSTRACT

This systematic review and meta-analysis aim to synthesize the existing literature to clarify the role of amino acids as potential indicators or contributors to childhood obesity. The study follows the PRISMA 2020 guidelines. A comprehensive search was conducted across multiple electronic databases, including PubMed, Cochrane Library, Embase, Web of Science, Google Scholar, Semantic Scholar, and ResearchRabbit, using relevant keywords such as "childhood obesity," "amino acids," and "branched-chain amino acids (BCAAs)."Heterogeneity among studies was assessed using the chi-square test and the I² statistic. Publication bias was evaluated using funnel plots and Egger's test. Five studies involving a total of 1,229 participants met the inclusion criteria. A significant association was observed between amino acid levels and obesity in children. Specifically, glutamine was inversely associated with obesity (SMD = -0.48, 95% CI: -0.85 to -0.11), while leucine (SMD = 0.79, 95% CI: 0.20 to 1.38) and valine (SMD = 0.67, 95% CI: 0.18 to 1.15) were positively associated. Additionally, odds ratio analysis indicated that higher glutamine levels were associated with 56% lower odds of obesity (OR = 0.44, 95% CI: 0.21-0.94, P < .01), suggesting a potential protective role. Elevated levels of specific amino acids, particularly BCAAs, were consistently linked to increased body mass index (BMI) and other obesity-related indicators in children. Future research should focus on longitudinal and interventional studies to better understand these associations and explore targeted strategies involving amino acid metabolism to help prevent and manage childhood obesity.

PMID:40304146 | DOI:10.4274/jcrpe.galenos.2025.2024-11-11

Andrology. 2025 Apr 30. doi: 10.1111/andr.70053. Online ahead of print.

ABSTRACT

Male factor infertility (MFI) is involved in half of the cases of couple infertility. The follicle-stimulating hormone (FSH) therapy is considered efficient to improve semen parameters and pregnancy rate in patients with idiopathic MFI, following the lesson learned from hypogonadotropic hypogonadism. However, while in patients with hypogonadotropic hypogonadism FSH therapy, in combination with human chorionic gonadotropin (hCG), is a well-established treatment, in patients with MFI the effects of the FSH therapy are variable and unpredictable. The FSH therapy in MFI should be a personalized treatment, tailored on the characteristics of the male patient and the couple. The pivotal aspect is the accurate identification of patients who might benefit from such treatment (responders) from those who might not (nonresponders). To date, selection of patients to be treated is based on history, physical examination, semen analysis, and hormonal assessment. However, these parameters cannot adequately identify a priori responder patients. Furthermore, tailored management should include pharmacological adaptation (dosage and duration of the therapy), as happens during ovarian hyperstimulation in assisted reproductive technologies. In a fully personalized therapy, pharmacogenetic factors must be considered. In this paper, we describe the evidence dealing with the pharmacogenetics of the FSH therapy in MFI, presenting the physiological and physiopathological basis and the pharmacogenetics studies dealing with effects of polymorphisms in the beta-subunit of FSH (FSHB) and the FSH receptor (FSHR) gene. According to the evidence so far available, genetic evaluation of FSHB and FSHR is recommended only for research purposes, since the data are not conclusive and even contrasting. Furthermore, the evidence so far is derived from quite small studies with different endpoints considered and relatively few cases. Better studies that consider the combined effect of several FSHB and FSHR gene polymorphisms, together with clinical, biochemical, seminal and testicular cytology, are necessary to develop an algorithm that might predict the response to the FSH treatment.

PMID:40304702 | DOI:10.1111/andr.70053

Clin Pharmacol Ther. 2025 Apr 30. doi: 10.1002/cpt.3702. Online ahead of print.

ABSTRACT

Older adults (65 years and over) frequently manage complex medication regimens and are vulnerable to adverse drug reactions and treatment inefficacies, some of which could be preventable with pharmacogenetics (PGx)-guided prescribing. This study examined the prevalence of actionable PGx genotypes (i.e., those linked to a guideline that recommends a change to standard prescribing), the use of cautionary medications (i.e., those associated with an actionable PGx genotype), polypharmacy (i.e., ≥ 5 medications simultaneously), and cytochrome P450 enzyme inhibitor and inducer use among 13,670 older adults enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) trial. Genotyping was conducted for 10 pharmacogenes with actionable PGx-based prescribing guidelines. Medication data were collected annually and assessed to identify cautionary medication use in the cohort. Most participants (98.8%) carried at least one actionable PGx genotype, with an average of three actionable genotypes per participant. VKORC1 (61.1%) and CYP2C19 (59.6%) were the most frequently observed genes with actionable genotypes. Statins (29.3%), nonsteroidal anti-inflammatory drugs (14.2%), and proton-pump inhibitors (7.9%) were the most used cautionary medications, with 27.5% of participants taking at least one medication for which PGx guidelines recommended a deviation from standard prescribing. Most (83.9%) participants reported taking a polypharmacy regimen, and 68.2% reported use of at least one cytochrome P450 enzyme inhibitor or inducer during the trial. Our findings underscore the high prevalence of actionable PGx genotypes, polypharmacy, and use of inhibitors and inducers in older adults, which collectively have the potential to inform safer and more effective prescribing practices.

PMID:40304392 | DOI:10.1002/cpt.3702

Methods Mol Biol. 2025;2908:51-64. doi: 10.1007/978-1-0716-4434-8_4.

ABSTRACT

The cystic fibrosis transmembrane conductance regulator (CFTR), a chloride channel residing primarily at the apical membrane of epithelial cells, plays a major role in fluid secretion and the maintenance of epithelial surface hydration. Mutations in the CFTR gene lead to the fatal disease known as cystic fibrosis (CF). Drugs that improve mutant CFTR protein folding and channel function have dramatically improved CF patient outcomes. However, the current regimen only restores the function of the most common mutant, ΔF508, to ~62% of wildtype (WT). Notably, ~10% of patients harboring hundreds of less common CFTR mutations are not eligible or do not respond at all to treatment with current CFTR modulators. Better characterizing the WT and mutant CFTR protein interactomes could provide critical insight into how to treat patients with rarer mutations and thereby improve the druggability of this devastating disease. Here we describe how BioID (proximity-dependent biotin identification) can be used to map the CFTR interactome in a human airway model-bronchial epithelial cells grown at the air-liquid interface. Approximately 26% (>5500) of all human protein-coding genes are predicted to code for membrane proteins, which together account for ~30% of the druggable proteome. The methods described here could thus also be applied to improve our understanding of many additional respiratory, autoimmune, and metabolic diseases.

PMID:40304902 | DOI:10.1007/978-1-0716-4434-8_4

Cancer Res. 2025 Apr 30. doi: 10.1158/0008-5472.CAN-24-4190. Online ahead of print.

ABSTRACT

Accurate preoperative assessment of lymph node metastasis (LNM) and overall survival (OS) status is essential for patients with locally advanced gastric cancer (LAGC) receiving neoadjuvant chemotherapy (NAC), providing timely guidance for clinical decision-making. However, current approaches to evaluate LNM and OS have limited accuracy. In this study, we used longitudinal CT images from 1,021 LAGC patients to develop and validate a multi-task deep learning model named co-attention tri-oriented spatial Mamba (CTSMamba) to simultaneously predict LNM and OS. CTSMamba was trained and validated on 398 patients, and the performance was further validated on 623 patients at two additional centers. Notably, CTSMamba exhibited significantly more robust performance than a clinical model in predicting LNM across all of the cohorts. Additionally, integrating CTSMamba survival scores with clinical predictors further improved personalized OS prediction. These results support the potential of CTSMamba to accurately predict LNM and OS from longitudinal images, potentially providing clinicians with a tool to inform individualized treatment approaches and optimized prognostic strategies.

PMID:40305075 | DOI:10.1158/0008-5472.CAN-24-4190

Med Phys. 2025 Apr 30. doi: 10.1002/mp.17859. Online ahead of print.

ABSTRACT

BACKGROUND: The presence of metal objects in computed tomography (CT) imaging introduces severe artifacts that degrade image quality and hinder accurate diagnosis. While several deep learning-based metal artifact reduction (MAR) methods have been proposed, they often exhibit poor performance on unseen data and require large datasets to train neural networks.

PURPOSE: In this work, we propose a sinogram inpainting method for metal artifact reduction that leverages a neural attenuation field (NAF) as a prior. This new method, dubbed NAFMAR, operates in a self-supervised manner by optimizing a model-based neural field, thus eliminating the need for large training datasets.

METHODS: NAF is optimized to generate prior images, which are then used to inpaint metal traces in the original sinogram. To address the corruption of x-ray projections caused by metal objects, a 3D forward projection of the original corrupted image is performed to identify metal traces. Consequently, NAF is optimized using a metal trace-masked ray sampling strategy that selectively utilizes uncorrupted rays to supervise the network. Moreover, a metal-aware loss function is proposed to prioritize metal-associated regions during optimization, thereby enhancing the network to learn more informed representations of anatomical features. After optimization, the NAF images are rendered to generate NAF prior images, which serve as priors to correct original projections through interpolation. Experiments are conducted to compare NAFMAR with other prior-based inpainting MAR methods.

RESULTS: The proposed method provides an accurate prior without requiring extensive datasets. Images corrected using NAFMAR showed sharp features and preserved anatomical structures. Our comprehensive evaluation, involving simulated dental CT and clinical pelvic CT images, demonstrated the effectiveness of NAF prior compared to other prior information, including the linear interpolation and data-driven convolutional neural networks (CNNs). NAFMAR outperformed all compared baselines in terms of structural similarity index measure (SSIM) values, and its peak signal-to-noise ratio (PSNR) value was comparable to that of the dual-domain CNN method.

CONCLUSIONS: NAFMAR presents an effective, high-fidelity solution for metal artifact reduction in 3D tomographic imaging without the need for large datasets.

PMID:40305006 | DOI:10.1002/mp.17859

Discov Oncol. 2025 Apr 30;16(1):645. doi: 10.1007/s12672-025-02279-8.

ABSTRACT

Skin Cancer is an extensive and possibly dangerous disorder that requires early detection for effective treatment. Add specific global statistics on skin cancer prevalence and mortality to emphasize the importance of early detection. Example: "Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. Manual skin cancer screening is both time-intensive and expensive. Deep learning (DL) techniques have shown exceptional performance in various applications and have been applied to systematize skin cancer diagnosis. However, training DL models for skin cancer diagnosis is challenging due to limited available data and the risk of overfitting. Traditionally approaches have High computational costs, a lack of interpretability, deal with numerous hyperparameters and spatial variation have always been problems with machine learning (ML) and DL. An innovative method called adaptive learning has been developed to overcome these problems. In this research, we advise an intelligent computer-aided system for automatic skin cancer diagnosis using a two-stage transfer learning approach and Pre-trained Convolutional Neural Networks (CNNs). CNNs are well-suited for learning hierarchical features from images. Annotated skin cancer photographs are utilized to detect ROIs and reset the initial layer of the pre-trained CNN. The lower-level layers learn about the characteristics and patterns of lesions and unaffected areas by fine-tuning the model. To capture high-level, global features specific to skin cancer, we replace the fully connected (FC) layers, responsible for encoding such features, with a new FC layer based on principal component analysis (PCA). This unsupervised technique enables the mining of discriminative features from the skin cancer images, effectively mitigating overfitting concerns and letting the model adjust structural features of skin cancer images, facilitating effective detection of skin cancer features. The system shows great potential in facilitating the initial screening of skin cancer patients, empowering healthcare professionals to make timely decisions regarding patient referrals to dermatologists or specialists for further diagnosis and appropriate treatment. Our advanced adaptive fine-tuned CNN approach for automatic skin cancer diagnosis offers a valuable tool for efficient and accurate early detection. By leveraging DL and transfer learning techniques, the system has the possible to transform skin cancer diagnosis and improve patient outcomes.

PMID:40304929 | DOI:10.1007/s12672-025-02279-8

Jpn J Ophthalmol. 2025 Apr 30. doi: 10.1007/s10384-025-01205-3. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the retinal age gap, defined as the difference between deep learning-predicted retinal age and chronological age, as a potential biomarker of systemic health in the Japanese population.

STUDY DESIGN: Prospective cohort study.

METHODS: Data from the Nagahama Study, a large-scale Japanese cohort study, were used. Participants were divided into fine-tuning (n=2,261) and analysis (n=6,070) cohorts based on their visit status across the two periods. The fine-tuning cohort only included individuals without a history of systemic or cardiovascular diseases. A deep learning model, originally released in the Japan Ocular Imaging Registry, was fine-tuned using a fine-tuning cohort to predict retinal age from images. This refined model was then applied to the analysis cohort to calculate retinal age gaps. We conducted cross-sectional and longitudinal analyses to examine the association of these gaps with systemic and cardiovascular diseases.

RESULTS: The retinal age-prediction model achieved a mean absolute error of 3.00-3.42 years. Cross-sectional analysis revealed significant associations between the retinal age gap and a history of diabetes (β = 1.08, p < 0.001) and hyperlipidemia (β = -0.67, p < 0.001). Longitudinal analysis showed no significant association between the baseline retinal age gap and disease onset. However, onset of hypertension (β = 0.35, p = 0.049) and hyperlipidemia (β = 0.34, p = 0.035) showed marginal associations with an increase in retinal age gap over time.

CONCLUSION: The retinal age gap is a promising biomarker for systemic health, particularly in relation to diabetes, hypertension, and hyperlipidemia.

PMID:40304887 | DOI:10.1007/s10384-025-01205-3

Brain Inform. 2025 Apr 30;12(1):11. doi: 10.1186/s40708-025-00256-z.

ABSTRACT

Recent advancements in cloud-based machine learning (ML) now allow for the rapid and remote identification of neural-biomarkers associated with common neuro-developmental disorders from neuroimaging datasets. Due to the sensitive nature of these datasets, secure deep learning (DL) algorithms are essential. Although, fully homomorphic encryption (FHE)-based methods have been proposed to maintain data confidentiality and privacy, however, existing FHE deep convolutional neural network (CNN) models still face some issues such as low accuracy, high encryption/decryption latency, energy inefficiency, long feature extraction times, and significant cipher-image expansion. To address these issues, this study introduces the HoRNS-CNN model, which integrates the energy-efficient features of the residue number system FHE scheme (RNS-FHE scheme) with the high accuracy of pre-trained deep CNN models in the cloud for efficient, privacy-preserving predictions and provide some proofs of its energy efficiency and homomorphism. The RNS-FHE scheme's FPGA implementation includes embedded RNS pixel-bitstream homomorphic encoder/decoder circuits for encrypting 8-bit grayscale pixels, with cloud CNN models performing remote classification on the encrypted images. In the HoRNS-CNN architecture, the ReLU activation functions of deep CNNs were initially trained for stability and later adapted for homomorphic computations using a Taylor polynomial approximation of degree 3 and batch normalization to achieve high accuracy. The findings show that the HoRNS-CNN model effectively manages cipher-image expansion with an asymptotic complexity of O n 3 , offering better performance and faster feature extraction compared to its peers. The model can predict 400,000 neural-biomarker features in one hour, providing an effective tool for analyzing neuroimages while ensuring privacy and security.

PMID:40304880 | DOI:10.1186/s40708-025-00256-z

Brain Inform. 2025 Apr 30;12(1):10. doi: 10.1186/s40708-025-00257-y.

ABSTRACT

Despite significant advancements in brain tumor classification, many existing models suffer from complex structures that make them difficult to interpret. This complexity can hinder the transparency of the decision-making process, causing models to rely on irrelevant features or normal soft tissues. Besides, these models often include additional layers and parameters, which further complicate the classification process. Our work addresses these limitations by introducing a novel methodology that combines Explainable AI (XAI) techniques with a Convolutional Neural Network (CNN) architecture. The major contribution of this paper is ensuring that the model focuses on the most relevant features for tumor detection and classification, while simultaneously reducing complexity, by minimizing the number of layers. This approach enhances the model's transparency and robustness, giving clear insights into its decision-making process through XAI techniques such as Gradient-weighted Class Activation Mapping (Grad-Cam), Shapley Additive explanations (Shap), and Local Interpretable Model-agnostic Explanations (LIME). Additionally, the approach demonstrates better performance, achieving 99% accuracy on seen data and 95% on unseen data, highlighting its generalizability and reliability. This balance of simplicity, interpretability, and high accuracy represents a significant advancement in the classification of brain tumor.

PMID:40304860 | DOI:10.1186/s40708-025-00257-y

Abdom Radiol (NY). 2025 Apr 30. doi: 10.1007/s00261-025-04966-0. Online ahead of print.

ABSTRACT

BACKGROUND: The prognosis and treatment outcomes for patients with rectal cancer are critically dependent on an accurate and comprehensive preoperative evaluation.Three-dimensional endorectal ultrasound (3D-ERUS) has demonstrated high accuracy in the T staging of rectal cancer. Thus, we aimed to develop a computer-aided diagnosis (CAD) tool using a deep learning model for the preoperative T-staging of rectal cancer with 3D-ERUS.

METHODS: We retrospectively analyzed the data of 216 rectal cancer patients who underwent 3D-ERUS. The patients were randomly assigned to a training cohort (n = 156) or a testing cohort (n = 60). Radiologists interpreted the 3D-ERUS images of the testing cohort with and without the CAD tool. The diagnostic performance of the CAD tool and its impact on the radiologists' interpretations were evaluated.

RESULTS: The CAD tool demonstrated high diagnostic efficacy for rectal cancer tumors of all T stages, with the best diagnostic performance achieved for T1-stage tumors (AUC, 0.85; 95% CI, 0.73-0.93). With assistance from the CAD tool, the AUC for T1 tumors improved from 0.76 (95% CI, 0.63-0.86) to 0.80 (95% CI, 0.68-0.94) (P = 0.020) for junior radiologist 2. For junior radiologist 1, the AUC improved from 0.61 (95% CI, 0.48-0.73) to 0.79 (95% CI, 0.66-0.88) (P = 0.013) for T2 tumors and from 0.73 (95% CI, 0.60-0.84) to 0.84 (95% CI, 0.72-0.92) (P = 0.038) for T3 tumors. The diagnostic consistency (κ value) also improved from 0.31 to 0.64 (P = 0.005) for the junior radiologists and from 0.52 to 0.66 (P = 0.005) for the senior radiologists.

CONCLUSION: A CAD tool utilizing a deep learning model based on 3D-ERUS images showed strong performance in T staging rectal cancer. This tool could improve the performance of and consistency between radiologists in preoperatively assessing rectal cancer patients.

PMID:40304753 | DOI:10.1007/s00261-025-04966-0

Orbit. 2025 Apr 30:1-7. doi: 10.1080/01676830.2025.2497460. Online ahead of print.

ABSTRACT

PURPOSE: To develop and validate a deep learning model for the detection of functional blepharoptosis from external ocular photographs, and to quantify the impact of augmenting the training data with synthetic images on model performance.

METHODS: External ocular photographs of 771 eyes from patients aged ≥ 21 years seen at a tertiary oculoplastic clinic. including 639 with clinically diagnosed functional blepharoptosis and 132 without, were obtained and cropped. These were then randomly assigned into training (n = 539), validation (n = 76) and test (n = 156) subsets, to train and evaluate a baseline deep learning model. Additional synthetic data from a pretrained StyleGAN model was then used to augment the training set (n = 2000), to train and evaluate an augmented deep learning model. Analysis of the performance of both models was then performed.

RESULTS: Accuracy of the deep learning models was assessed in terms of sensitivity and specificity in identifying eye images with functionally significant blepharoptosis. A sensitivity of 0.68 (0.60-0.76), specificity of 0.89 (0.77-1.00) and AUC of 0.87 (0.81-0.93) was obtained by the baseline model, and a sensitivity of 0.95 (0.92-0.99), specificity of 0.67 (0.49-0.84) and AUC of 0.91 (0.86-0.96) by the GAN augmented model.

CONCLUSIONS: Functional blepharoptosis can be detected from external ocular photographs with high confidence, and the use of synthetic data from generative models has the potential to further improve the model performance.

PMID:40304715 | DOI:10.1080/01676830.2025.2497460

Radiol Artif Intell. 2025 May;7(3):e250224. doi: 10.1148/ryai.250224.

NO ABSTRACT

PMID:40304577 | DOI:10.1148/ryai.250224

Neurosurgery. 2025 Apr 30. doi: 10.1227/neu.0000000000003466. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Machine learning (ML) in surgical video analysis offers promising prospects for training and decision support in surgery. The past decade has seen key advances in ML-based operative workflow analysis, though existing applications mostly feature shorter surgeries (<2 hours) with limited scene changes. The aim of this study was to develop and evaluate a ML model capable of automated operative workflow recognition for retrosigmoid vestibular schwannoma (VS) resection. In doing so, this project furthers previous research by applying workflow prediction platforms to lengthy (median >5 hours duration), data-heavy surgeries, using VS resection as an exemplar.

METHODS: A video dataset of 21 microscopic retrosigmoid VS resections was collected at a single institution over 3 years and underwent workflow annotation according to a previously agreed expert consensus (Approach, Excision, and Closure phases; and Debulking or Dissection steps within the Excision phase). Annotations were used to train a ML model consisting of a convolutional neural network and a recurrent neural network. 5-fold cross-validation was used, and performance metrics (accuracy, precision, recall, F1 score) were assessed for phase and step prediction.

RESULTS: Median operative video time was 5 hours 18 minutes (IQR 3 hours 21 minutes-6 hours 1 minute). The "Tumor Excision" phase accounted for the majority of each case (median 4 hours 23 minutes), whereas "Approach and Exposure" (28 minutes) and "Closure" (17 minutes) comprised shorter phases. The ML model accurately predicted operative phases (accuracy 81%, weighted F1 0.83) and dichotomized steps (accuracy 86%, weighted F1 0.86).

CONCLUSION: This study demonstrates that our ML model can accurately predict the surgical phases and intraphase steps in retrosigmoid VS resection. This demonstrates the successful application of ML in operative workflow recognition on low-volume, lengthy, data-heavy surgical videos. Despite this, there remains room for improvement in individual step classification. Future applications of ML in low-volume high-complexity operations should prioritize collaborative video sharing to overcome barriers to clinical translation.

PMID:40304484 | DOI:10.1227/neu.0000000000003466

Chem Commun (Camb). 2025 Apr 30. doi: 10.1039/d5cc00961h. Online ahead of print.

ABSTRACT

Mitochondrial autophagy is closely related to abnormal NAD(P)H and pH. Here, we synthesized a dual-response fluorescent probe with high selectivity for NAD(P)H and sensitivity in the physiological pH range, for simultaneous imaging analysis of mitochondrial NAD(P)H and pH, holding potential as a novel tool for understanding mitochondria-associated diseases.

PMID:40305089 | DOI:10.1039/d5cc00961h

Elife. 2025 Apr 30;12:RP90668. doi: 10.7554/eLife.90668.

ABSTRACT

The Staphylococcus aureus clonal complex 8 (CC8) is made up of several subtypes with varying levels of clinical burden; from community-associated methicillin-resistant S. aureus USA300 strains to hospital-associated (HA-MRSA) USA500 strains and ancestral methicillin-susceptible (MSSA) strains. This phenotypic distribution within a single clonal complex makes CC8 an ideal clade to study the emergence of mutations important for antibiotic resistance and community spread. Gene-level analysis comparing USA300 against MSSA and HA-MRSA strains have revealed key horizontally acquired genes important for its rapid spread in the community. However, efforts to define the contributions of point mutations and indels have been confounded by strong linkage disequilibrium resulting from clonal propagation. To break down this confounding effect, we combined genetic association testing with a model of the transcriptional regulatory network (TRN) to find candidate mutations that may have led to changes in gene regulation. First, we used a De Bruijn graph genome-wide association study to enrich mutations unique to the USA300 lineages within CC8. Next, we reconstructed the TRN by using independent component analysis on 670 RNA-sequencing samples from USA300 and non-USA300 CC8 strains which predicted several genes with strain-specific altered expression patterns. Examination of the regulatory region of one of the genes enriched by both approaches, isdH, revealed a 38-bp deletion containing a Fur-binding site and a conserved single-nucleotide polymorphism which likely led to the altered expression levels in USA300 strains. Taken together, our results demonstrate the utility of reconstructed TRNs to address the limits of genetic approaches when studying emerging pathogenic strains.

PMID:40305082 | DOI:10.7554/eLife.90668

Proc Natl Acad Sci U S A. 2025 May 6;122(18):e2427151122. doi: 10.1073/pnas.2427151122. Epub 2025 Apr 30.

ABSTRACT

In the common cellular space, hundreds of binding reactions occur reliably and simultaneously without disruptive mutual interference. The design principles that enable this remarkable compatibility have not yet been adequately elucidated. In order to delineate these principles, we consider the intracellular sensing of transition metals in bacteria-an integral part of cellular metal homeostasis. Protein cytosolic sensors typically interact with metals through three types of lateral chain residues, containing oxygen, nitrogen, or sulfur. The very existence of complete sets of mutually compatible sensors is a nontrivial problem solved by evolution, since each metal sensor has to bind to its cognate metal without being "mismetallated" by noncognate competitors. Here, based solely on theoretical considerations and limited information about binding constants for metal-amino acid interactions, we are able to predict possible "sensor compositions," i.e., the residues forming the binding sites. We find that complete transition-metal sensor sets are severely limited in their number by compatibility requirements, leaving only a handful of possible sensor compositions for each transition metal. Our theoretical results turn out to be broadly consistent with experimental data on known bacterial sensors. If applicable to other cytosolic binding interactions, the results generated by our approach imply that compatibility requirements may play a crucial role in the organization and functioning of intracellular processes.

PMID:40305046 | DOI:10.1073/pnas.2427151122