BMC Med Educ. 2025 Jan 14;25(1):63. doi: 10.1186/s12909-025-06651-8.

ABSTRACT

BACKGROUND: Individuals often respond differently to medications, giving rise to the field of precision medicine (PM), which focuses on tailoring treatments to individual genetic, environmental, and lifestyle factors. This study examined the level of comfort healthcare professional students have with their knowledge of precision medicine, alongside their attitudes and perceptions toward precision medicine, at a tertiary institution in Nigeria.

METHODS: A cross-sectional questionnaire-based study was conducted among healthcare professional students (400-600 level) at the University of Nigeria Nsukka between January and March 2024. The data were analyzed via IBM Statistical Product and Service Solutions (SPSS) for Windows version 27. Descriptive analyses (frequency, percentage, mean, and standard deviation) and chi-square tests were used to summarize and compare the variables. Statistical significance was set at p < 0.05.

RESULTS: A total of 431 healthcare professional students participated in this study. Fewer than half (n = 200, 46.4%) were pharmacy students, and the majority were within the age range of 21-25 years (n = 288, 66.8%). Nearly half (n = 206, 47.8%) reported having information about precision medicine from the internet, and the majority (n = 341, 79.1%) expressed having an interest in a career involving research in precision medicine. More than half of the students (n = 240, 55.7%) were comfortable with their knowledge of precision medicine and had favourable attitudes (n = 236, 54.8%). Additionally, more than half had positive perceptions of ethical concerns (n = 216, 50.1%) and education in precision medicine (n = 239, 55.5%). Gender, age, department, level of study, awareness of PM, and interest in a career involving research were significantly associated with students' knowledge, attitudes, and perceptions of precision medicine (p < 0.001).

CONCLUSION: Healthcare professional students were comfortable with their knowledge of PM and, in addition, had favourable attitudes and positive perceptions toward the use of precision medicine.

PMID:39806360 | DOI:10.1186/s12909-025-06651-8

Sci Rep. 2025 Jan 13;15(1):1824. doi: 10.1038/s41598-024-84744-y.

ABSTRACT

This study was designed to assess the effect of brentuximab vedotin on several breast cancer cell lines in terms of promoting apoptosis and managing cancer progression. Additionally, the study investigated the potential of repurposing this drug for new therapeutic reasons, beyond its original indications. The study evaluates the cytotoxic effects of Brentuximab vedotin across five cell lines: normal human skin fibroblasts (HSF), three breast cancer cell lines (MCF-7, MDA-MB-231, and T-47D), and histiocytic lymphoma (U-937). Brentuximab treatment was administered at four time points (0, 24, 48, and 72 h), with cell viability assessed at each interval. HSF cells, serving as controls, exhibited minimal viability loss (above 70%), indicating limited toxicity in normal fibroblasts. In contrast, MCF-7 and MDA-MB-231 cells demonstrated time-dependent reductions in viability, with a pronounced decline by 72 h, suggesting Brentuximab's efficacy in both ER-positive and triple-negative breast cancer. T-47D cells also showed decreased viability, though at a slower rate. U-937 cells exhibited the most substantial reduction, highlighting Brentuximab's potent activity against hematologic malignancies. Wound healing assays further revealed that Brentuximab significantly impaired the migration and healing capacity of cancer cells compared to untreated controls. Additionally, cell cycle analysis indicated G2/M phase arrest in cancer cells, particularly in MCF-7 and MDA-MB-231, while HSF cells remained largely unaffected. Apoptosis detection confirmed Brentuximab-induced cell death, with significant increases in late apoptosis in cancer lines, especially by 72 h. Gene expression analysis revealed upregulation of pro-apoptotic genes (BAX, Caspase 3, and Caspase 9) in cancer cells, alongside a decrease in anti-apoptotic BCL-2 expression. These findings suggest Brentuximab's selective cytotoxicity against cancer cells and its potential as an effective therapeutic agent, particularly in breast cancer and histiocytic lymphoma.

PMID:39805861 | DOI:10.1038/s41598-024-84744-y

BMJ Support Palliat Care. 2025 Jan 13:spcare-2024-005205. doi: 10.1136/spcare-2024-005205. Online ahead of print.

ABSTRACT

CONTEXT: Pharmacogenomics (PGx) is an area of expanding research, which could indicate whether an individual is likely to benefit from a symptom control medication. Palliative and supportive care (PSC) could be an area that benefits from PGx, however, little is known about the current evidence base for this.

OBJECTIVE: To determine how PGx can be applied in PSC, whether there is any evidence of benefit, and to understand the extent and type of evidence that supports the use of PGx in PSC.

METHODS: A search of six databases up to July 2024. Reference snowballing from review articles and screened papers was used to identify any missed articles.

RESULTS: 11 articles were reviewed. A total of 550 patients had a PGx test across 8/11 studies. Up to half of the patients had an actionable PGx result, and in one study there were 4.6 drug-gene interactions per patient. Implementation of PGx was found to be feasible. Clinician adherence to advice given was under-reported. No studies reported health economics analysis, or was designed to definitively answer whether PGx was better than standard care.

CONCLUSIONS: It is both feasible and acceptable to conduct PGx testing in a supportive and palliative care setting. Many supportive care medications are amenable to PGx. Clinician adherence to recommendations is variable and there is no clear evidence that PGx enhances palliative/supportive care patient outcomes. Prospective, clinical trials are needed to establish whether PGx can improve symptom management for people receiving PSC.

PMID:39805678 | DOI:10.1136/spcare-2024-005205

Nat Rev Drug Discov. 2025 Jan 13. doi: 10.1038/d41573-025-00008-y. Online ahead of print.

NO ABSTRACT

PMID:39806010 | DOI:10.1038/d41573-025-00008-y

Sci Rep. 2025 Jan 13;15(1):1849. doi: 10.1038/s41598-025-85948-6.

ABSTRACT

Approaches to mitigate the severity of infections and of immune responses are still needed for the treatment of cystic fibrosis (CF) even with the success of highly effective modulator therapies. Previous studies identified reduced levels of melatonin in a CF mouse model related to circadian rhythm dysregulation. Melatonin is known to have immunomodulatory properties and it was hypothesized that treatment with melatonin would improve responses to bacterial infection in CF mice. Data demonstrate that CF mice (G542X/G542X) treated with melatonin (10 µg/mL) in drinking water for 10 weeks had improved responses to airway infection with a clinical isolate of Pseudomonas aeruginosa. Melatonin-treated mice exhibited improved bacterial clearance, reduced inflammatory markers. Mice treated in drinking water for 1 week had improved bacterial clearance but no improvement in inflammation. Wild type (WT) control mice showed no response to melatonin treatment suggesting melatonin is eliciting a CF-specific response in this model. The efficacy of direct melatonin (1 µM) treatment to the airways was also tested and found to be ineffective. In conclusion, long-term systemic treatment with melatonin is an effective therapy in a CF mouse model that normalizes the response to airway infection to a WT pattern.

PMID:39805903 | DOI:10.1038/s41598-025-85948-6

NPJ Biofilms Microbiomes. 2025 Jan 13;11(1):14. doi: 10.1038/s41522-024-00644-z.

ABSTRACT

Biofilms are critical in the persistence of Pseudomonas aeruginosa infections, particularly in cystic fibrosis patients. This study explores the adaptive mechanisms behind the phenotypic switching between Small Colony Variants (SCVs) and revertant states in P. aeruginosa biofilms, emphasizing hypermutability due to Mismatch Repair System (MRS) deficiencies. Through experimental evolution and whole-genome sequencing, we show that both wild-type and mutator strains undergo parallel evolution by accumulating compensatory mutations in factors regulating intracellular c-di-GMP levels, particularly in the Wsp and Yfi systems. While wild-type strains face genetic constraints, mutator strains bypass these by accessing alternative genetic pathways regulating c-di-GMP and biofilm formation. This increased genetic accessibility, driven by higher mutation rates and specific mutational biases, supports sustained cycles of SCV conversion and reversion. Our findings underscore the crucial role of hypermutability in P. aeruginosa adaptation, with significant implications for managing persistent infections in clinical settings.

PMID:39805827 | DOI:10.1038/s41522-024-00644-z

Arch Dis Child Educ Pract Ed. 2025 Jan 13:edpract-2024-328203. doi: 10.1136/archdischild-2024-328203. Online ahead of print.

ABSTRACT

Transformative changes in the health of children with cystic fibrosis (CF) mean that more sensitive outcome measures are needed to monitor paediatric CF lung disease. Multiple breath washout (MBW) and its primary readout lung clearance index are gaining increasing traction as an endpoint for clinical trials in the CF space and show promise as a clinical investigation. In this article, we use four clinically based questions to explore what MBW can and cannot (yet) do and highlight some of its strengths and weaknesses as an investigation. We end by discussing how we can increase the utility of MBW as an investigation in children with CF.

PMID:39805677 | DOI:10.1136/archdischild-2024-328203

Lancet Respir Med. 2025 Jan 10:S2213-2600(24)00364-3. doi: 10.1016/S2213-2600(24)00364-3. Online ahead of print.

ABSTRACT

BACKGROUND: Bronchiectasis is a disease with a global impact, but most published data come from high-income countries. We aimed to describe the clinical characteristics of patients with bronchiectasis in China.

METHODS: The Chinese Bronchiectasis Registry (BE-China) is a prospective, observational cohort enrolling patients from 111 hospitals in China. Data on demographics, comorbidities, and aetiological testing results were collected from adult patients with bronchiectasis at baseline and annual follow-up. Patients who met the inclusion criteria (age ≥18 years; received chest high-resolution CT in the past year showing bronchiectasis affecting one or more lung lobes; and clinical history consistent with bronchiectasis, including chronic cough, daily sputum production, and history of exacerbations) were included. Patients with known cystic fibrosis were excluded. To investigate variations according to different economic regions, two groups were compared based on whether per capita disposable income of residents was greater than US$5553. Clinical characteristics were compared with the European (EMBARC) registry and other national registries.

FINDINGS: Between Jan 10, 2020, and March 31, 2024, 10 324 patients from 97 centres were included in the study. Among 9501 participants with available data, the most common cause of bronchiectasis was post-infective disease (4101 [43·2%] patients), followed by idiopathic (2809 [29·6%] patients). 6676 (70·0%) of 9541 patients with available data had at least one exacerbation in the year before enrolment and 5427 (57·2%) of 9489 patients with available data were hospitalised at least once due to exacerbations. Treatments commonly used in high-income countries, such as inhaled antibiotics and macrolides, were infrequently used in China. Implementation of airway clearance in China was scarce, with only 1177 (12·2%) of 9647 patients having used at least one method of airway clearance. Compared with upper-middle-income regions, patients from lower-middle-income regions were younger (61·0 years [SD 14·0] vs 63·9 years [14·2]) with a higher proportion of pulmonary comorbidities (521 [17·8%] of 2922 patients vs 639 [8·6%] of 7402 with chronic obstructive pulmonary disease and 194 [6·6%] of 2922 patients vs 364 [4·9%] of 7402 patients with asthma), a higher tuberculosis burden (442 [16·0%] of 2768 patients vs 715 [10·6%] of 6733 patients), more severe radiological involvement (1160 [42·4%] of 2736 patients vs 2415 [35·4%] of 6816 patients with cystic bronchiectasis), more exacerbations (median 1·4 [IQR 0-2] in both groups; mean 1·4 [SD 1·6] vs 1·2 [1·4] in the previous year) and hospitalisations (1662 [60·6%] of 2743 patients vs 3765 [55·8%] of 6746 patients hospitalised at least once in the previous year), and poorer quality of life (median 57·4 [IQR 53·5-63·1] vs 58·7 [54·8-64·8] assessed by the Bronchiectasis Health Questionnaire).

INTERPRETATION: The clinical characteristics of patients with bronchiectasis in China show differences compared with cohorts in Europe and India. Bronchiectasis is more severe with a higher burden of exacerbations in lower-income regions. The management of patients with bronchiectasis in China urgently needs standardisation and improvement.

FUNDING: National Natural Science Foundation of China, Innovation Program of the Shanghai Municipal Education Commission, Program of the Shanghai Municipal Science and Technology Commission, and Program of the Shanghai Shenkang Development Center.

TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

PMID:39805296 | DOI:10.1016/S2213-2600(24)00364-3

Lancet Respir Med. 2025 Jan 10:S2213-2600(24)00427-2. doi: 10.1016/S2213-2600(24)00427-2. Online ahead of print.

NO ABSTRACT

PMID:39805295 | DOI:10.1016/S2213-2600(24)00427-2

Semin Thromb Hemost. 2025 Jan 13. doi: 10.1055/s-0044-1801825. Online ahead of print.

ABSTRACT

The purpose of this study is to (1) estimate and compare the prevalence of venous thromboembolism (VTE) in children (age 0 to ≤21) with versus without cystic fibrosis (CF); (2) investigate putative associations between specific gastrointestinal (GI) manifestations and the development of VTE among children with CF. This was a multicenter case-control analysis among patients aged 0 to ≤ 21 years between 2010 and 2020, using the TriNetX Research Network. Data queries included ICD-9/10 (International Classification of Diseases-9th/10th Revision) diagnosis codes. Bivariate associations with VTE among CF patients were compared using Chi-square testing for categorical variables and Student's t-test for continuous variables. We used multivariable logistic regression to test for independent associations of GI manifestations with VTE among children with CF, with adjustment for other salient covariates. There was a total of 7,689 children with and 22,327,660 without CF. The frequency of occurrence of VTE was increased nearly 20-fold among those with, as compared with without CF (130 vs. 7 per 10,000 patients). Acute pancreatitis (adjusted odd ratio [aOR] = 3.80, [95% confidence interval, CI: 2.00-7.22]), biliary disease (aOR = 2.17 [95% CI: 1.17-4.03]), gastrostomy status (aOR = 2.01 [95% CI: 1.27-3.18]), and malabsorption/malnutrition (aOR = 2.41 [95% CI: 1.52-3.82]) were each associated with a higher likelihood of VTE among children with CF. In conclusion, we found a significantly increased frequency of VTE occurrence and association of specific GI diseases as independent risk factors for VTE among children with CF compared with those without.

PMID:39805291 | DOI:10.1055/s-0044-1801825

Knee Surg Relat Res. 2025 Jan 13;37(1):3. doi: 10.1186/s43019-024-00256-z.

ABSTRACT

BACKGROUND: Unplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility. This study aims to compare the predictive accuracy of the SRC with a novel artificial neural network (ANN) algorithm for 30-day readmission after primary TKA, using the same set of clinical variables from a large national database.

METHODS: Patients undergoing primary TKA between 2013 and 2020 were identified from the ACS-NSQIP database and randomly stratified into training and validation cohorts. The ANN was developed using data from the training cohort with fivefold cross-validation performed five times. ANN and SRC performance were subsequently evaluated in the distinct validation cohort, and predictive performance was compared on the basis of discrimination, calibration, accuracy, and clinical utility.

RESULTS: The overall cohort consisted of 365,394 patients (trainingN = 362,559; validationN = 2835), with 11,392 (3.1%) readmitted within 30 days. While the ANN demonstrated good discrimination and calibration (area under the curve (AUC)ANN = 0.72, slope = 1.32, intercept = -0.09) in the validation cohort, the SRC demonstrated poor discrimination (AUCSRC = 0.55) and underestimated readmission risk (slope = -0.21, intercept = 0.04). Although both models possessed similar accuracy (Brier score: ANN = 0.03; SRC = 0.02), only the ANN demonstrated a higher net benefit than intervening in all or no patients on the decision curve analysis. The strongest predictors of readmission were body mass index (> 33.5 kg/m2), age (> 69 years), and male sex.

CONCLUSIONS: This study demonstrates the superior predictive ability and potential clinical utility of the ANN over the conventional SRC when constrained to the same variables. By identifying the most important predictors of readmission following TKA, our findings may assist in the development of novel clinical decision support tools, potentially improving preoperative counseling and postoperative monitoring practices in at-risk patients.

PMID:39806502 | DOI:10.1186/s43019-024-00256-z

BMC Med Educ. 2025 Jan 13;25(1):56. doi: 10.1186/s12909-024-06556-y.

ABSTRACT

BACKGROUND AND PURPOSE: The purpose of reflection in the learning process is to create meaningful and deep learning. Considering the importance of emphasizing active and student-centered methods in learning and the necessity of learners' participation in the education process, the present study was conducted to investigate the effect of flipped classroom teaching method on the amount of reflection ability in nursing students and the course of professional ethics.

STUDY METHOD: The current study is a quasi-experimental study using Solomon's four-group method. The statistical population included all nursing students who were taking the professional ethics course at Kermanshah University of Medical Sciences. The study tool was a 26-item questionnaire with acceptable validity and reliability. The sample size was 80 nursing students by simple random method and divided into four groups, which included: 1- experimental group 1 2- experimental group 2 3- control group 1, and control 2. The collected data were used by SPSS software and using descriptive statistics methods and two-way analysis of variance and analysis of covariance analysis.

FINDINGS: The findings showed that the four investigated groups do not have statistically significant differences in terms of gender composition (p = 0.599). There was no significant difference between the control and experimental groups in terms of all 5 reflection components in the pre-test. A significant difference was observed between the amount of reflection of the experimental and control groups.

CONCLUSION: Considering that there are controversial issues in the course of professional ethics, this method can be effective in the field of deep learning of students.

PMID:39806386 | DOI:10.1186/s12909-024-06556-y

BMC Med Imaging. 2025 Jan 13;25(1):17. doi: 10.1186/s12880-025-01554-y.

ABSTRACT

BACKGROUND: Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality.

METHODS: We enrolled a cohort of sixty patients who underwent DL-MRI, conventional MRI, and No-DL MRI examinations to evaluate image quality. Key metrics considered in the assessment included scan duration, overall image quality, quantitative assessments of Relative Signal-to-Noise Ratio (rSNR), Relative Contrast-to-Noise Ratio (rCNR), and diagnostic efficacy. Two experienced radiologists independently assessed image quality using a 5-point scale (5 indicating the highest quality). To gauge interobserver agreement for the assessed pathologies across image sets, we employed weighted kappa statistics. Additionally, the Wilcoxon signed rank test was employed to compare image quality and quantitative rSNR and rCNR measurements.

RESULTS: Scan time was significantly reduced with DL-MRI and represented an approximate 66.5% reduction. DL-MRI consistently exhibited superior image quality in both coronal T2WI and axial T2WI when compared to both conventional MRI (p < 0.01) and No-DL-MRI (p < 0.01). Interobserver agreement was robust, with kappa values exceeding 0.735. For rSNR data, coronal fat-saturated(FS) T2WI and axial FS T2WI in DL-MRI consistently outperformed No-DL-MRI, with statistical significance (p < 0.01) observed in all cases. Similarly, rCNR data revealed significant improvements (p < 0.01) in coronal FS T2WI of DL-MRI when compared to No-DL-MRI. Importantly, our findings indicated that DL-MRI demonstrated diagnostic performance comparable to conventional MRI.

CONCLUSION: Integrating deep learning-based reconstruction methods into standard clinical workflows has the potential to the promise of accelerating image acquisition, enhancing image clarity, and increasing patient throughput, thereby optimizing diagnostic efficiency.

TRIAL REGISTRATION: Retrospectively registered.

PMID:39806303 | DOI:10.1186/s12880-025-01554-y

BMC Bioinformatics. 2025 Jan 13;26(1):13. doi: 10.1186/s12859-025-06040-4.

ABSTRACT

BACKGROUND: MicroRNAs (miRNAs) are pivotal in the initiation and progression of complex human diseases and have been identified as targets for small molecule (SM) drugs. However, the expensive and time-intensive characteristics of conventional experimental techniques for identifying SM-miRNA associations highlight the necessity for efficient computational methodologies in this field.

RESULTS: In this study, we proposed a deep learning method called Multi-source Data Fusion and Graph Neural Networks for Small Molecule-MiRNA Association (MDFGNN-SMMA) to predict potential SM-miRNA associations. Firstly, MDFGNN-SMMA extracted features of Atom Pairs fingerprints and Molecular ACCess System fingerprints to derive fusion feature vectors for small molecules (SMs). The K-mer features were employed to generate the initial feature vectors for miRNAs. Secondly, cosine similarity measures were computed to construct the adjacency matrices for SMs and miRNAs, respectively. Thirdly, these feature vectors and adjacency matrices were input into a model comprising GAT and GraphSAGE, which were utilized to generate the final feature vectors for SMs and miRNAs. Finally, the averaged final feature vectors were utilized as input for a multilayer perceptron to predict the associations between SMs and miRNAs.

CONCLUSIONS: The performance of MDFGNN-SMMA was assessed using 10-fold cross-validation, demonstrating superior compared to the four state-of-the-art models in terms of both AUC and AUPR. Moreover, the experimental results of an independent test set confirmed the model's generalization capability. Additionally, the efficacy of MDFGNN-SMMA was substantiated through three case studies. The findings indicated that among the top 50 predicted miRNAs associated with Cisplatin, 5-Fluorouracil, and Doxorubicin, 42, 36, and 36 miRNAs, respectively, were corroborated by existing literature and the RNAInter database.

PMID:39806287 | DOI:10.1186/s12859-025-06040-4

BMC Cancer. 2025 Jan 13;25(1):75. doi: 10.1186/s12885-024-13423-y.

ABSTRACT

BACKGROUND: Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis is crucial to improve patient outcomes. Dermoscopy, a non-invasive imaging technique, aids in melanoma detection but can be limited by subjective interpretation. Recently, machine learning and deep learning techniques have shown promise in enhancing diagnostic precision by automating the analysis of dermoscopy images.

METHODS: This systematic review examines recent advancements in machine learning (ML) and deep learning (DL) applications for melanoma diagnosis and prognosis using dermoscopy images. We conducted a thorough search across multiple databases, ultimately reviewing 34 studies published between 2016 and 2024. The review covers a range of model architectures, including DenseNet and ResNet, and discusses datasets, methodologies, and evaluation metrics used to validate model performance.

RESULTS: Our results highlight that certain deep learning architectures, such as DenseNet and DCNN demonstrated outstanding performance, achieving over 95% accuracy on the HAM10000, ISIC and other datasets for melanoma detection from dermoscopy images. The review provides insights into the strengths, limitations, and future research directions of machine learning and deep learning methods in melanoma diagnosis and prognosis. It emphasizes the challenges related to data diversity, model interpretability, and computational resource requirements.

CONCLUSION: This review underscores the potential of machine learning and deep learning methods to transform melanoma diagnosis through improved diagnostic accuracy and efficiency. Future research should focus on creating accessible, large datasets and enhancing model interpretability to increase clinical applicability. By addressing these areas, machine learning and deep learning models could play a central role in advancing melanoma diagnosis and patient care.

PMID:39806282 | DOI:10.1186/s12885-024-13423-y

Oral Radiol. 2025 Jan 14. doi: 10.1007/s11282-025-00804-7. Online ahead of print.

ABSTRACT

OBJECTIVES: Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture.

MATERIALS AND METHODS: The first stage involved tooth localization with the YOLOv8 model, followed by pulp stone classification using ResNeXt. 375 panoramic images were included in this study, and a comprehensive set of evaluation metrics, including precision, recall, false-negative rate, false-positive rate, accuracy, and F1 score was employed to rigorously assess the performance of the proposed architecture.

RESULTS: Despite the limited annotated training data, the proposed method achieved impressive results: an accuracy of 95.4%, precision of 97.1%, recall of 96.1%, false-negative rate of 3.9%, false-positive rate of 6.1%, and a F1 score of 96.6%, outperforming existing approaches in pulp stone detection.

CONCLUSIONS: Unlike current studies, this approach adopted a more realistic scenario by utilizing a small dataset with few annotated samples, acknowledging the time-consuming and error-prone nature of expert labeling. The proposed system is particularly beneficial for dental students and newly graduated dentists who lack sufficient clinical experience, as it aids in the automatic detection of pulpal calcifications. To the best of our knowledge, this is the first study in the literature that propose a pipeline architecture to address the PS detection tasks on panoramic images.

PMID:39806222 | DOI:10.1007/s11282-025-00804-7

Eur J Hum Genet. 2025 Jan 13. doi: 10.1038/s41431-024-01782-w. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) has been growing more powerful and accessible, and will increasingly impact many areas, including virtually all aspects of medicine and biomedical research. This review focuses on previous, current, and especially emerging applications of AI in clinical genetics. Topics covered include a brief explanation of different general categories of AI, including machine learning, deep learning, and generative AI. After introductory explanations and examples, the review discusses AI in clinical genetics in three main categories: clinical diagnostics; management and therapeutics; clinical support. The review concludes with short, medium, and long-term predictions about the ways that AI may affect the field of clinical genetics. Overall, while the precise speed at which AI will continue to change clinical genetics is unclear, as are the overall ramifications for patients, families, clinicians, researchers, and others, it is likely that AI will result in dramatic evolution in clinical genetics. It will be important for all those involved in clinical genetics to prepare accordingly in order to minimize the risks and maximize benefits related to the use of AI in the field.

PMID:39806188 | DOI:10.1038/s41431-024-01782-w

Surg Endosc. 2025 Jan 13. doi: 10.1007/s00464-024-11486-3. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to develop an automated skills assessment tool for surgical trainees using deep learning.

BACKGROUND: Optimal surgical performance in robot-assisted surgery (RAS) is essential for ensuring good surgical outcomes. This requires effective training of new surgeons, which currently relies on supervision and skill assessment by experienced surgeons. Artificial Intelligence (AI) presents an opportunity to augment existing human-based assessments.

METHODS: We used a network architecture consisting of a convolutional neural network combined with a long short-term memory (LSTM) layer to create two networks for the extraction and analysis of spatial and temporal features from video recordings of surgical procedures, facilitating action recognition and skill assessment.

RESULTS: 21 participants (16 novices and 5 experienced) performed 16 different intra-abdominal robot-assisted surgical procedures on porcine models. The action recognition network achieved an accuracy of 96.0% in identifying surgical actions. A GradCAM filter was used to enhance the model interpretability. The skill assessment network had an accuracy of 81.3% in classifying novices and experiences. Procedure plots were created to visualize the skill assessment.

CONCLUSION: Our study demonstrated that AI can be used to automate surgical action recognition and skill assessment. The use of a porcine model enables effective data collection at different levels of surgical performance, which is normally not available in the clinical setting. Future studies need to test how well AI developed within a porcine setting can be used to detect errors and provide feedback and actionable skills assessment in the clinical setting.

PMID:39806176 | DOI:10.1007/s00464-024-11486-3

Methods Mol Biol. 2025;2887:79-89. doi: 10.1007/978-1-0716-4314-3_5.

ABSTRACT

SNARE proteins play a pivotal role in membrane fusion and various cellular processes. Accurate identification of SNARE proteins is crucial for elucidating their functions in both health and disease contexts. This chapter presents a novel approach employing multiscan convolutional neural networks (CNNs) combined with position-specific scoring matrix (PSSM) profiles to accurately recognize SNARE proteins. By leveraging deep learning techniques, our method significantly enhances the accuracy and efficacy of SNARE protein classification. We detail the step-by-step methodology, including dataset preparation, feature extraction using PSI-BLAST, and the design of the multiscan CNN architecture. Our results demonstrate that this approach outperforms existing methods, providing a robust and reliable tool for bioinformatics research.

PMID:39806147 | DOI:10.1007/978-1-0716-4314-3_5

Med Biol Eng Comput. 2025 Jan 13. doi: 10.1007/s11517-024-03266-x. Online ahead of print.

ABSTRACT

In the context of chronic liver diseases, where variability in progression necessitates early and precise diagnosis, this study addresses the limitations of traditional histological analysis and the shortcomings of existing deep learning approaches. A novel patch-level classification model employing multi-scale feature extraction and fusion was developed to enhance the grading accuracy and interpretability of liver biopsies, analyzing 1322 cases across various staining methods. The study also introduces a slide-level aggregation framework, comparing different diagnostic models, to efficiently integrate local histological information. Results from extensive validation show that the slide-level model consistently achieved high F1 scores, notably 0.9 for inflammatory activity and steatosis, and demonstrated rapid diagnostic capabilities with less than one minute per slide on average. The patch-level model also performed well, with an F1 score of 0.64 for ballooning and 0.99 for other indicators, and proved transferable to public datasets. The conclusion drawn is that the proposed analytical framework offers a reliable basis for the diagnosis and treatment of chronic liver diseases, with the added benefit of robust interpretability, suggesting its practical utility in clinical settings.

PMID:39806118 | DOI:10.1007/s11517-024-03266-x