Invest New Drugs. 2025 Apr 28. doi: 10.1007/s10637-025-01533-8. Online ahead of print.

ABSTRACT

HP518 is an oral PROteolysis TArgeting Chimera (PROTAC) protein degrader targeting the wild-type androgen receptor (WT-AR) and mutant AR ligand-binding domain (AR-LBD). A multicenter, first-in-human, open-label Phase 1 dose escalation study was conducted in patients with metastatic castration-resistant prostate cancer (mCRPC) to evaluate the safety, pharmacokinetics, and anti-tumor activity of HP518. Twenty-two patients with mCRPC with disease progression on at least 1 novel androgen receptor pathway inhibitor (ARPI) and ≤ 1 line of chemotherapy received HP518 once daily orally in sequential cohorts. Patients were not selected for AR-LBD mutations. Objectives were to assess safety, tolerability, maximum tolerated dose, pharmacokinetics (PK), as well as efficacy by PSA50 response and radiographic response per Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 and Prostate Cancer Working Group 3 (PCWG3) criteria. Exploratory objectives included genomic profiling using cell-free DNA. The majority of treatment-emergent adverse events (TEAEs) were Grade 1 or 2. The most common AEs were nausea and vomiting, fatigue, constipation, diarrhea, and decreased appetite. Only one (vomiting) out of 10 serious adverse events (SAE) was considered drug-related. No patient experienced a dose-limiting toxicity (DLT), and no AEs led to dose reduction or study discontinuation. Following multiple dosing of HP518, the PK appeared to plateau showing a less than dose-proportional relationship between exposure and dosage. Two patients demonstrated a partial response, and three patients showed a PSA50 response. In this initial Phase 1 study, HP518 demonstrated an acceptable safety profile and responses in a limited subset of mCRPC patients with progression after ARPI warranting further investigation. ClinicalTrials.gov Identifier: NCT05252364.

PMID:40289067 | DOI:10.1007/s10637-025-01533-8

Int J Infect Dis. 2025 Apr 25:107914. doi: 10.1016/j.ijid.2025.107914. Online ahead of print.

ABSTRACT

OBJECTIVES: This study assessed adverse event with tuberculosis preventive therapy (TPT) regimens among individuals with latent tuberculosis infection (LTBI).

METHODS: Using national health insurance data, we analyzed individuals newly diagnosed with LTBI between 2015 and 2020. The TPT group, prescribed 3 months of isoniazid and rifampicin (3HR), 4 months of rifampicin (4R), or 9 months of isoniazid (9H), was matched with a control group through 1:1 propensity score matching. Drug-related adverse events were reported.

RESULTS: Of 220,483 diagnosed with LTBI, 49.0% received TPT, primarily 3HR (74.6%). The incidence of any adverse events with TPT was 11.90%, with 8.94% of these events being severe events requiring hospitalization. Hepatotoxicity risk was 6.48-, 4.79-, and 3.50-fold with 3HR, 9H, and 4R, respectively, compared to controls. Severe cutaneous adverse reaction risk was 4.27-, 1.83-, and 1.93-fold with 3HR, 9H, and 4R. 4R had the lowest risk of any adverse events, while 3HR had the highest. Permanent discontinuation occurred in 2.3%, 3.1%, and 3.3% with 4R, 9H, and 3HR, respectively. Unlike 9H, rifampicin-based regimens showed no age-related trend in adverse event risk.

CONCLUSIONS: 4R is a better option considering safety across a broad age range, suggesting it could be encouraged in the LTBI population.

PMID:40288747 | DOI:10.1016/j.ijid.2025.107914

Adv Sci (Weinh). 2025 Apr 27:e2504855. doi: 10.1002/advs.202504855. Online ahead of print.

ABSTRACT

Terahertz metasurface biosensors based on the quasi-bound state in the continuum (QBIC) offer label-free, rapid, and ultrasensitive biomedical detection. Recent advances in deep learning facilitate efficient, fast, and customized design of such metasurfaces. However, prior approaches primarily establish one-to-one mappings between structure and optical response, neglecting the trade-offs among key performance indicators. This study proposes a pioneering method leveraging transfer learning to optimize multiple indicators in metasurface biosensor design. For the first time, multiple-indicator comprehensive optimization of the quality (Q) factor, figure of merit (FoM), and effective sensing area (ESA) is achieved. The two-stage transfer learning method pre-trains on low-dimensional datasets to extract shared features, followed by fine-tuning on complex, high-dimensional tasks. By adopting frequency shift as a unified criterion, the contribution ratios of these indicators are quantified as 26.09% for the Q factor, 48.42% for FoM, and 25.49% for ESA. Compared to conventional deep-learning approaches, the proposed method reduces data requirements by 50%. The biosensor designed using this method detects the biomarker homocysteine, achieving detection at the ng µL-1 level, with experimental results closely matching theoretical predictions. This work establishes a novel paradigm for metasurface biosensor design, paving the way for transformative advances in trace biological detection.

PMID:40287969 | DOI:10.1002/advs.202504855

Cell Rep. 2025 Apr 25;44(5):115623. doi: 10.1016/j.celrep.2025.115623. Online ahead of print.

ABSTRACT

Tissue-resident macrophages (TRMs) populate throughout various tissues, and their homeostatic metabolism is heavily influenced by these microenvironments. Peroxisomes are organelles that contribute to lipid metabolism. However, the involvement of these organelles in the bioenergetics of TRMs remains undetermined. We conducted a developmental screen of TRMs using a conditional peroxisomal biogenesis factor 5 (Pex5) knockout mouse model that lacks functional peroxisomes in all immune cell subsets. Pulmonary alveolar macrophages (AMs) appeared as the only subset of TRMs that required functional peroxisomes for their development. Pex5 deficiency resulted in reduced AM survival due to increased sensitivity to lipotoxicity, in line with an excess accumulation of ceramides. The absence of peroxisomes had a significant effect on overall mitochondrial fitness and altered their metabolic program, allowing them to engage in glycolysis in addition to oxidative phosphorylation. Our results revealed that AMs have a unique metabolic regulation, where peroxisomes play a central role in their homeostatic development and maintenance.

PMID:40287943 | DOI:10.1016/j.celrep.2025.115623

Hum Mol Genet. 2025 Apr 27:ddaf033. doi: 10.1093/hmg/ddaf033. Online ahead of print.

ABSTRACT

Biobanks are innovative biomedical research infrastructures that play a crucial role in advancing cancer research by supporting investigations into the etiology, progression, and therapeutic interventions of the disease. Biobanks have significantly contributed to personalized medicine by providing high-quality bio specimen resources and expertise in tissue handling, essential for understanding the interplay of genetic, ecological, and lifestyle factors on cancer biology, human health, and mortality. By linking bio specimens with clinical, pathological, and epidemiological data, biobanks are central in the discovery and development of cancer therapeutics through biomarkers. In this review, the importance of managing biobanks as integral parts of data generation and analytics continuum driving precision medicine is pointed out. The advent of multi-OMICS analytics, combined with artificial intelligence, systems biology, and deep machine learning, has elevated the importance of bio banking human bio specimens as not only a biological resource but also an informatics asset. Here, we examine the impact of bio banking in facilitating translational, bench-to-bedside cancer research, with a focus on multi-OMICS data-driven biomarker discovery and precision oncology. In addition, we discuss one of the major innovations in biobank management: the hub-and-spoke model. This centralized system leverages core expertise and resources while collecting bio specimens from diverse geographic regions, thereby capturing the heterogeneity of cancer biology. The hub-and-spoke approach is particularly advantageous for countries like India, characterized by vast geographic and demographic diversity. It ensures complete coverage of the different types of cancers, disease stages, and population groups in addressing the complexity and diversity of cancer biology.

PMID:40287834 | DOI:10.1093/hmg/ddaf033

Nucleic Acids Res. 2025 Apr 26:gkaf350. doi: 10.1093/nar/gkaf350. Online ahead of print.

ABSTRACT

Circular dichroism (CD) spectroscopy is an established biophysical technique to study chiral molecules. CD allows investigating conformational changes under varying experimental conditions and has been used to understand secondary structure, folding, and binding of proteins and nucleic acids. Here, we present ChiraKit, a user-friendly, online, and open-source tool to process raw CD data and perform advanced analysis. ChiraKit features include the calculation of protein secondary structure with the SELCON3 and SESCA algorithms, estimation of peptide helicity using the helix-ensemble model, the fitting of thermal/chemical unfolding or user-defined models, and the decomposition of spectra through singular value decomposition or principal component analysis. ChiraKit can be accessed at https://spc.embl-hamburg.de/.

PMID:40287821 | DOI:10.1093/nar/gkaf350

Psychiatry Res. 2025 Jul;349:116509. doi: 10.1016/j.psychres.2025.116509. Epub 2025 Apr 19.

ABSTRACT

People presenting to centres for rare diseases (CRD) for diagnostic work-up often suffer from mental disorders. The prevalence and distribution of these mental disorders and their relevance for care remain largely unclear and well-controlled multicentre studies are missing. The ZSE-DUO study was a multicentre, prospective, controlled cohort study involving 11 German CRD. In total, 662 adult patients with an unclear diagnosis were evaluated by an additional mental health specialist along with their usual CRD care. Mental disorders were assessed through a standardized clinical examination, including the Mini-DIPS interview. Prevalence of diagnosed mental disorders (ICD-10 coding) was assessed and compared to population prevalence. A total of 54.5 % (361 patients) of adults with unexplained symptoms presenting to a CRD had current mental disorders. Mental disorders were deemed the sole explanation for the entire symptomatology in 53.5 % of cases. In 36.2 % of cases, a combination of a mental disorder with a somatic disease was considered to explain the unexplained symptoms. In 8.3 % of cases, it was assessed that the mental disorder was not involved in explaining the unexplained symptoms. Assessing whether a mental disorder contributes to the patient's symptom complex is crucial for determining suitable treatment strategies in terms of a bio-psycho-social approach.

PMID:40286780 | DOI:10.1016/j.psychres.2025.116509

Molecules. 2025 Mar 31;30(7):1548. doi: 10.3390/molecules30071548.

ABSTRACT

Predicting the toxicity of drug molecules using in silico quantitative structure-activity relationship (QSAR) approaches is very helpful for guiding safe drug development and accelerating the drug development procedure. The ongoing development of machine learning techniques has made this task easier and more accurate, but it still suffers negative effects from both the severely skewed distribution of active/inactive chemicals and relatively high-dimensional feature distribution. To simultaneously address both of these issues, a binary ant colony optimization feature selection algorithm, called BACO, is proposed in this study. Specifically, it divides the labeled drug molecules into a training set and a validation set multiple times; with each division, the ant colony seeks an optimal feature group that aims to maximize the weighted combination of three specific class imbalance performance metrics (F-measure, G-mean, and MCC) on the validation set. Then, after running all divisions, the frequency of each feature (descriptor) that emerges in the optimal feature groups is calculated and ranked in descending order. Only those high-frequency features are used to train a support vector machine (SVM) and construct the structure-activity relationship (SAR) prediction model. The experimental results for the 12 datasets in the Tox21 challenge, represented by the Modred descriptor calculator, show that the proposed BACO method significantly outperforms several traditional feature selection approaches that have been widely used in QSAR analysis. It only requires a few to a few dozen descriptors for most datasets to exhibit its best performance, which shows its effectiveness and potential application value in cheminformatics.

PMID:40286190 | PMC:PMC11990530 | DOI:10.3390/molecules30071548

Diagn Pathol. 2025 Apr 26;20(1):53. doi: 10.1186/s13000-025-01654-x.

ABSTRACT

OBJECTIVE: This case report describes a patient with acute fibrinous and organising pneumonia (AFOP) presenting with mass-like imaging on chest computed tomography (CT), aiming to enhance clinical awareness of this rare disease.

CASE PRESENTATION: A 66-year-old man presented with cough, sputum, chest tightness and weight loss persisting for 1 month. Chest X-ray revealed a space-occupying lesion in the left lung. Further CT imaging demonstrated irregular soft tissue masses in both the upper and lower lobes of the left lung. Although the imaging findings suggested lung cancer, the final pathological diagnosis confirmed AFOP. The patient was treated with methylprednisolone, resulting in substantial improvement of the upper lobe lesion, whereas the lower lobe lesion showed minimal response. Following the addition of mycophenolate mofetil, the lower lobe lesion decreased substantially. Multiple lung biopsies confirmed the diagnosis of AFOP, with no evidence of a malignant tumour.

CONCLUSIONS: Acute fibrinous and organising pneumonia presents with non-specific imaging findings, and when manifesting as a mass-like lesion, it may be misdiagnosed as lung cancer. Pathological examination remains essential for diagnosis. Close monitoring of the clinical response is crucial during treatment, and the treatment plan should be tailored to individual patient needs.

PMID:40287744 | DOI:10.1186/s13000-025-01654-x

Immunol Allergy Clin North Am. 2025 May;45(2):223-249. doi: 10.1016/j.iac.2025.01.010.

ABSTRACT

With their groundbreaking clinical responses, immune checkpoint inhibitors (ICIs) have ushered in a new chapter in cancer therapeutics. However, they are often associated with life-threatening or organ-threatening autoimmune/autoinflammatory phenomena, collectively termed immune-related adverse events (irAEs). In this review, we will first describe the mechanisms of action of ICIs as well as irAEs. Next, we will review biomarkers for predicting the development of irAEs or stratifying risks.

PMID:40287170 | DOI:10.1016/j.iac.2025.01.010

BMC Med Res Methodol. 2025 Apr 26;25(1):114. doi: 10.1186/s12874-025-02559-5.

ABSTRACT

INTRODUCTION: Rare events data have proven difficult to explain and predict. Standard statistical procedures can sharply underestimate the probability of rare events, such as intravenous immune globulin therapy (IVIg) for bullous pemphigoid.

METHODS: This retrospective cross-sectional study used Department of Defense TRICARE data to determine factors associated with IVIg therapy among bullous pemphigoid patients. We used prior and weighted correction methods for logit regression to solve rare event bias.

RESULTS: We identified 2,720 individuals diagnosed with bullous pemphigoid from 2019 to 2022, of which 14 were treated with IVIg. Patients who received IVIg therapy were younger (65.07 vs. 75.85, P =.0016) and more likely to be female (13 vs. 1, P =.0036). The underestimation with the standard regression model for event probabilities ranged from 11% to 102% using the prior correction method and from 15% to 107% using the weighted correction method.

CONCLUSION: Rare events are low-frequency, high-severity problems that can have significant consequences. Rare diseases and rare therapies are individually unique but collectively contribute to substantial health and social needs. Therefore, correct estimation of the events is the first step toward assessing the burden of rare diseases and the pricing of their therapies.

PMID:40287629 | DOI:10.1186/s12874-025-02559-5

Int J Pharm. 2025 Apr 24:125639. doi: 10.1016/j.ijpharm.2025.125639. Online ahead of print.

ABSTRACT

The current research work explored the anti-inflammatory and antifungal activity of loratadine versus Candida albicans for treating vulvovaginal candidiasis (VVC). Loratadine was incorporated into terpene-enriched nanocarrier invasomes (IVS) using a thin-film hydration approach, where a D-optimal design was employed to investigate the lipid amount, terpene percent and type impact on the entrapment efficiency percentage (EE%), particle size (PS), polydispersity index (PDI), and zeta potential (ZP). The optimal formula was assessed regarding morphology, Fourier transform infrared (FTIR), in silico study, and differential scanning calorimetry (DSC). Subsequently, the optimal formula was incorporated into a gel base and characterized via ex vivo permeation studies. Lastly, in vivo investigations were performed to evaluate the performance of the loratadine-loaded IVS gel. The selected formula was spherical and had EE% of 91.95 ± 1.5 %, PS of 132.81 ± 2.7 nm, PDI of 0.326 ± 0.08, and ZP of -23.42 ± 0.38 mV. The FTIR and DSC studies verified the successful entrapment of the drug, while the in silico study demonstrated the thermodynamic stability of IVS. The IVS gel showed enhancement in drug deposition within vaginal tissues of 3.8 folds compared to free drug gel. Loratadine-loaded IVS gel showed remarkable improvement in eradicating candida infection by suppression of β-D-glucan. Furthermore, the IVS gel showed a significant anti-inflammatory effect reflected by marked suppression of NO, iNOS, COX-2, TNF-α, IL-1B and down-regulation in NF-κB, Src and Syk gene expressions. These results were confirmed via histopathological and immunohistological examinations. This work highlights the potential of loratadine as a promising therapy for VVC.

PMID:40287073 | DOI:10.1016/j.ijpharm.2025.125639

Drug Discov Today. 2025 Apr 24:104364. doi: 10.1016/j.drudis.2025.104364. Online ahead of print.

ABSTRACT

The human metapneumovirus (hMPV), a member of the Pneumoviridae family, is a significant respiratory pathogen that causes severe infections in infants, children, the elderly, adults with chronic illnesses, and individuals with immunocompromised conditions. Globally, hMPV is recognized as the second leading cause of bronchiolitis and pneumonia among children under five. The absence of targeted antiviral treatments or vaccines for hMPV significantly strains the global health-care system. This review summarizes recent advances and scientific findings on hMPV by reviewing the current literature on its life cycle, structure, function, prevention, and treatment options.

PMID:40286981 | DOI:10.1016/j.drudis.2025.104364

Lancet Psychiatry. 2025 Apr 23:S2215-0366(25)00066-5. doi: 10.1016/S2215-0366(25)00066-5. Online ahead of print.

ABSTRACT

Existing pharmacotherapies and psychotherapies are often inadequate, and discovery for new pharmacological treatments in psychiatry is slow. Existing pharmacotherapies are, however, often inadequate. Few truly novel pharmacotherapies have emerged in the past four decades, largely due to the absence of a known pathophysiology for each disorder. In this Personal View, we describe the platform we have adopted that enables targeted drug repurposing. With this approach, patient-derived stem cells are used to detect transcriptomic targets to identify existing drugs that address these targets. These drugs are then validated in non-human animal models and pharmacoepidemiological studies before being tested in clinical trials. Our targeted drug repurposing platform bypasses the absence of known pathophysiology. Validation steps bring greater scientific rigour and mechanistic insights to drug repurposing to allow only drug candidates with the strongest mechanistic evidence to be tested in clinical trials.

PMID:40286796 | DOI:10.1016/S2215-0366(25)00066-5

J Cyst Fibros. 2025 Apr 26:S1569-1993(25)00770-2. doi: 10.1016/j.jcf.2025.04.004. Online ahead of print.

ABSTRACT

BACKGROUND: People with cystic fibrosis (pwCF) are living longer and with an increased risk of malignancies, preventative cancer screening is crucial. The objectives of this study were to determine cancer screening rates for pwCF compared to the general population, and assess the impact of primary care provider (PCP) involvement on screening rates among those with CF.

METHODS: This population-based cohort study linked Canadian CF Registry data with health administrative databases. Four screening cohorts were identified: breast, cervical, colorectal pre-transplant, colorectal post-transplant. PCP involvement was defined using billing codes. Screening rates were calculated as the number screened divided by the number of person-years individuals were eligible for screening. Poisson regression was used to describe rates.

RESULTS: In the CF cohort, 74/110 (67.3 %) were screened for breast cancer, and 321/541 (59.3 %) for cervical cancer. 186/402 (46.3 %) in the pre-transplant cohort were screened with colonoscopy and 75/148 (50.7 %) in the post-transplant cohort. Those with CF were significantly more likely to be screened for breast cancer (RR 3.39, 95 % CI 2.70-4.26) and colorectal cancer pre-transplant (RR 1.58, 95 % CI 1.37-1.82) compared to the non-CF cohort. Having a PCP increased the likelihood that pwCF received screening for breast cancer (RR 3.6, 95 % CI 1.13-11.44), cervical cancer (RR 1.71, 95 % CI 1.13-2.57), and colorectal cancer (pre-transplant population only) (RR 1.57, 95 % CI 1.06-2.32).

CONCLUSIONS: Screening rates for cancers in CF remain suboptimal. These results highlight opportunities to improve screening uptake through better integration of PCP in CF care models and to increase awareness of cancer risk.

PMID:40287331 | DOI:10.1016/j.jcf.2025.04.004

Clin Nutr ESPEN. 2025 Apr 24:S2405-4577(25)00274-8. doi: 10.1016/j.clnesp.2025.04.006. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: Elexacaftor, Tezacaftor, Ivacaftor has enhanced clinical outcomes, expected lifespan and quality of life for people with cystic fibrosis. Increased body mass index post- Elexacaftor, Tezacaftor, Ivacaftor is well documented, suggesting high-energy, high-fat recommendations may no longer be appropriate. This study aims to identify changes in dietary intake and nutritional parameters post gene modulator therapy Elexacaftor, Tezacaftor, Ivacaftor.

METHODS: A retrospective cohort study assessed diet and nutritional parameters of adults with cystic fibrosis (n=40) pre- and post- Elexacaftor, Tezacaftor, Ivacaftor (9 ± 3 months). Dietary intake was analysed (24hr recalls), for total energy intake, macronutrient distribution, sodium, core food groups, and discretionary intakes being compared to Nutrition Guidelines for people with cystic fibrosis, and general population guidelines.

RESULTS: Total energy intake decreased from 139.3 to 116.6kJ/kg/day (p=0.012) and was below the estimated energy requirement for people with cystic fibrosis by 4457.3kJ (p<0.001), despite body mass index increasing (p=<0.001). Median dietary sodium decreased from 1364mg to 1251mg (p=0.028). Intake of protein was above, and total fat intake met the higher end of guidelines for people with cystic fibrosis. Saturated fat intake did not change (p=0.403) however exceeded general guidelines of <10% total energy intake in line with general population intake of 15.9%. A decrease in discretionary foods (5.1 to 4.5 serves/d, p=0.038) and increase in vegetable intake (interquartile range 1.0-2.9 to 1.5-3.7 serves/d, p=0.036) was noted. Serum retinol levels increased from 1.7μmol/L to 2.0μmol/L (p=0.003).

CONCLUSION: Dietary patterns of people with cystic fibrosis change following Elexacaftor, Tezacaftor, Ivacaftor commencement. While total energy intake decreased and vegetable intake increased, saturated fat and discretionary intake remained high, increasing risk of metabolic disease. An individualised approach is needed with modulator therapy and recognition that changing diet is a modifiable risk factor for chronic disease.

PMID:40287067 | DOI:10.1016/j.clnesp.2025.04.006

Transplant Rev (Orlando). 2025 Apr 23;39(3):100933. doi: 10.1016/j.trre.2025.100933. Online ahead of print.

ABSTRACT

BACKGROUND: Whether survival differs between urgent lung transplantation (ULTx) and standard lung transplantation (LTx) remains unclear. This systematic review and meta-analysis aimed to evaluate survival and other post-transplant outcomes between ULTx and standard LTx.

METHODS: PubMed, Embase, and Cochrane Library were searched up to July 31, 2024 for relevant studies. A meta-analysis of baseline characteristics and postoperative outcomes was then performed, with subgroup analyses by study designs and indications. Overall survival (OS) was set as the primary outcome in this study. Risk ratio (RR), mean differences (MD) with 95 % confidence interval (CI) were assessed using fixed-effects or random-effects models.

RESULTS: Nine studies with 934 ULTx and 2980 standard LTx patients were included. ULTx group exhibited lower donor PaO2/FiO2 (P = 0.03) and higher pre-operative life support use (P < 0.001) than standard LTx group. No statistical difference in waiting list mortality was found between groups (28.4 % vs. 12.6 %; P = 0.54). ULTx was associated with significantly lower 1-year, 3-year, and 5-year OS than standard LTx (70.2 % vs. 80.0 %, 57.7 % vs. 66.7 %, 46.5 % vs. 56.2 %; all P < 0.001). At each time point, about 10 % OS rate differences were found consistently. In most subgroups, ULTx was associated with worse outcomes, but no difference in OS was observed in cystic fibrosis (CF) patients.

CONCLUSIONS: ULTx reduces waiting list mortality in critical patients, but is associated with worse OS than standard LTx. ULTx may limit short-term survival rather than long-term survival compared with standard LTx.

PMID:40286583 | DOI:10.1016/j.trre.2025.100933

World J Surg Oncol. 2025 Apr 26;23(1):164. doi: 10.1186/s12957-025-03819-w.

ABSTRACT

BACKGROUND: To develop and validate an MRI-based fusion model for preoperative prediction of perineural invasion (PNI) status in patients with intrahepatic cholangiocarcinoma (ICC).

METHODS: A retrospective collection of 192 ICC patients from three medical centers (training set: n = 147; external test set: n = 45) was performed. Patients were classified into the PNI-positive and PNI-negative groups based on postoperative pathological results. After image preprocessing, a total of 1,197 features were extracted from T2-weighted imaging (T2WI). Feature selection was performed, and a radiomics model was constructed using machine learning algorithms, followed by SHapley Additive exPlanations (SHAP) visualization. Subsequently, a deep learning model was constructed based on the pre-trained ResNet101, with Gradient-weighted Class Activation Mapping (Grad-CAM) used for visualization. Finally, a fusion model incorporating deep learning, radiomics, and clinical features was developed using logistic regression, and visualization was performed with a nomogram. The predictive performance of the model was evaluated based on the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS: The fusion model, which integrates deep learning signature, radiomics signature, and two clinical features, demonstrated strong discrimination for PNI status. In the training set, the AUC was 0.905, with an accuracy of 0.823; in the external test set, the AUC was 0.760, with an accuracy of 0.778. Visualization methods provided support for the practical application of the model.

CONCLUSION: The fusion model aids in the preoperative identification of PNI status in patients with ICC, and may help guide clinical decision-making regarding preoperative staging and adjuvant therapy.

PMID:40287750 | DOI:10.1186/s12957-025-03819-w

World J Surg Oncol. 2025 Apr 26;23(1):166. doi: 10.1186/s12957-025-03765-7.

ABSTRACT

Hepatocellular carcinoma (HCC) is a prevalent and lethal cancer, often diagnosed at advanced stages where traditional treatments such as surgical resection, liver transplantation, and locoregional therapies provide limited benefits. Hepatic arterial infusion chemotherapy (HAIC) has emerged as a promising treatment modality for advanced HCC, enhancing anti-tumor efficacy through targeted drug delivery while minimizing systemic side effects. However, the heterogeneous nature of HCC leads to variable responses to HAIC, highlighting the necessity for reliable predictive indicators to tailor personalized treatment strategies. This review explores the factors influencing HAIC success, including patient demographics, tumor characteristics, biomarkers, genomic profiles, and advanced imaging techniques such as radiomics and deep learning models. Additionally, the synergistic potential of HAIC combined with immunotherapy and molecular targeted therapies is examined, demonstrating improved survival outcomes. Prognostic scoring systems and nomograms that integrate clinical, molecular, and imaging data are discussed as superior tools for individualized prognostication compared to traditional staging systems. Understanding these predictors is essential for optimizing HAIC efficacy and enhancing survival and quality of life for patients with advanced HCC. Future research directions include large-scale prospective studies, integration of multi-omics data, and advancements in artificial intelligence to refine predictive models and further personalize treatment approaches.

PMID:40287734 | DOI:10.1186/s12957-025-03765-7

BMC Med Educ. 2025 Apr 26;25(1):619. doi: 10.1186/s12909-025-07171-1.

ABSTRACT

BACKGROUND: Problem-based learning (PBL) is expected to encourage a deep learning approach. Whether this is realised in practice remains uncertain. We investigated the relationships between learning approaches, academic achievement and student satisfaction in an integrated PBL curriculum, among students with diverse characteristics.

METHODS: All Year 1 students of an undergraduate UK medical programme, delivered concurrently at City St George's, University of London and the University of Nicosia, were invited to participate in 2019-2020 and 2020-2021. Students completed the validated Study Process Questionnaire (SPQ) at the beginning and end of Year 1. We explored changes in learning approaches and the associations of the learning approach with academic performance (in written and clinical examinations) and student satisfaction.

RESULTS: 129 students participated. Deep motivation decreased significantly over the year [Baseline: 11.03 ± 2.29; End of Year 1: 10.21 ± 2.26; p < 0.05). Graphical representations and tertile analysis further showed changes in individual learning approaches. Lower deep motivation scores were observed among male students, and those who were older, white, held biomedical sciences degrees, undergraduate degrees, or were native English speakers. Conversely, higher surface motivation was seen among female students, and those who were younger or held undergraduate degrees. Nicosia students became less strategic by the end of the year. No association was found between learning approach, or its change within the year, and examination performance. However, surface learning was negatively correlated with satisfaction regarding aspects of pharmacology learning in PBL and prescribing confidence. Strategic learners preferred lectures and had mixed perceptions about learning pharmacology in PBL, although they found student diversity facilitated their learning.

CONCLUSIONS: While PBL is expected to promote deep learning, our findings show that in a real-world context, these benefits are not consistently realised. Learners adopted less favourable learning approaches over the year, with increasing reliance on surface learning and less deep motivation. Such shifts may be due to excessive workload, assessment burden or curriculum uncertainty. We have identified student groups that may be more vulnerable to the stresses of a PBL setting, which may represent targets for intervention. Future studies may also investigate curriculum adaptations to enhance deep learning in a PBL curriculum.

PMID:40287686 | DOI:10.1186/s12909-025-07171-1