Cancer Pathog Ther. 2024 May 31;3(2):129-134. doi: 10.1016/j.cpt.2024.05.003. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: Whole-tissue autologous therapeutic vaccines (WATVs) are a form of cancer immunotherapy that use a patient's own pathological tissue. Concerns exist regarding the potential of WATVs to induce autoimmunity or the spread of cancer; however, their adverse events (AEs) have not been adequately studied. This literature review primarily aimed to evaluate the risks of autoimmunity and cancer seeding associated with using WATVs in human clinical trials. Its secondary objectives included assessing the incidence of AEs graded 1-5 using the Common Terminology Criteria for Adverse Events v5.0.

METHODS: The inclusion criteria were any clinical trial using human subjects in which at least part of the cancer vaccine was derived from the patient's own tumor tissue, which likely preserved the unique tumor-associated antigens (TAAs) present in the patient's tumor (i.e., whole-tissue). Tumor vaccine trials that used limited TAAs or highly processed tumor antigens were excluded. Published clinical trials were searched using Google Scholar until March 2024. The authors elaborated on the risk of bias in such cases, as indicated. All reviewed publications were searched for evidence of autoimmunity, cancer seeding, and other AEs. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement guided the review.

RESULTS: Data from 55 human clinical trials, abstracts, case reports, and unpublished data were analyzed, including 3323 patients treated with WATVs for various cancers. The primary outcomes were: (1) no documented cases of WATV-induced autoimmunity, (2) no documented cases of WATV-induced spreading or seeding of noninfectious cancers, and (3) the observed 0.24% (2/838) risk of spreading or seeding infectious cancers was attributed to inadequate sterilization. The secondary outcomes were: (1) no deaths were attributed to WATV therapy, (2) 0.18% (6/3323) incidence of grade 4 AEs, (3) 0.42% (14/3323) incidence of grade 3 AEs, (4) the incidence of grades 1-2 AEs was 52.21% (478/916).

CONCLUSIONS: WATVs carry no risk of inducing autoimmunity and essentially no risk of cancer seeding if properly sterilized. WATVs also exhibit a side effect profile comparable to that of routine vaccinations, with common, mild, and transient adverse effects. The combined risk of grade 3 and 4 AEs was 0.60% (20/3323). No deaths were causally associated with WATV treatment.

PMID:40182122 | PMC:PMC11963168 | DOI:10.1016/j.cpt.2024.05.003

BMC Pharmacol Toxicol. 2025 Apr 3;26(1):74. doi: 10.1186/s40360-025-00915-1.

ABSTRACT

BACKGROUND: Sickle cell disease (SCD) is a severe genetic disorder causing anemia, pain, and organ damage, affecting millions globally. Voxelotor, approved in the United States in 2019, targeted sickle cell disease pathophysiology. Despite its therapeutic benefits, concerns remain regarding its long-term safety and potential side effects, including headaches and gastrointestinal disturbances. This study used the FDA Adverse Event Reporting System (FAERS) to assess voxelotor's safety, aiming to enhance treatment strategies and clinical decision-making in SCD management.

METHODS: In this study, we utilized the FAERS to extract voxelotor-related adverse event reports from 2019 to 2024. We conducted descriptive and disproportionality analyses using four algorithms: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinkage (MGPS) to identify significant adverse event signals. The reliability of voxelotor adverse drug reactions (ADRs) was further improved by comparing with hydroxyurea ADRSs. Finally, adverse reactions were divided into acute ADRS, delayed ADRs and efficacy related reports to analyze the adverse event onset time.

RESULTS: A total of 16,677,340 case reports were collected in the FAERS database, of which 20,902 reports related to voxelotor were identified. Voxelotor induced adverse events occurred in 27 system organ categories (SOC). Key system organ classes affected were the blood and gastrointestinal systems. Notably, some adverse events, such as priapism and osteonecrosis, were not listed on the drug's label. The median adverse event onset time of acute ADRs, delayed ADRs and efficacy related reports were 1, 189.5 and 271 days, respectively.

CONCLUSION: This study systematically analyzed ADRs of voxelotor, highlighting the need for ongoing monitoring and further research on voxelotor's long-term safety and efficacy in treating sickle cell disease.

PMID:40181444 | DOI:10.1186/s40360-025-00915-1

J Transl Med. 2025 Apr 3;23(1):393. doi: 10.1186/s12967-024-06057-y.

ABSTRACT

BACKGROUND: Non-small cell lung cancer (NSCLC) is highly heterogeneous, leading to varied treatment responses and immune-related adverse reactions (irAEs) among patients. Habitat radiomics allows non-invasive quantitative assessment of intratumor heterogeneity (ITH). Therefore, our objective is to employ habitat radiomics techniques to develop a robust approach for predicting the efficacy of Immune checkpoint inhibitors (ICIs) and the likelihood of irAEs in advanced NSCLC patients.

METHODS: In this retrospective two center study, two independent cohorts of patients with NSCLC were used to develop (n = 248) and validate signatures (n = 95). After applying four kinds of machine learning algorithms to select the key preoperative CT radiomic features, we used clinical, radiomics and habitat radiomic features to develop the clinical signature, radiomics signature and habitat radiomic signature for ICIs prognostics and irAEs prediction. By combining habitat radiomic features with corresponding clinicopathologic information, the nomogram signature was constructed in the training cohort. Next, the internal validation cohort (n = 75) of patients, and the external validation cohort (n = 20) of patients treated with ICIs were included to evaluate the predictive value of the four signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC).

RESULTS: Our study introduces a radiomic nomogram model that integrates clinical and habitat radiomic features to identify patients who may benefit from ICIs or experience irAEs. The Radiomics Nomogram model exhibited superior predictive performance in the training, validation, and external validation sets, with AUCs of 0.923, 0.817, and 0.899, respectively. This model outperformed both the Whole-tumor Radiomics Signature model (AUCs of 0.870, 0.736, and 0.626) and the Habitat Signature model (AUCs of 0.900, 0.804, and 0.808). The radiomics model focusing on tumor sub-regional habitat showed better predictive performance than the model derived from the entire tumor. Decision Curve Analysis (DCA) and calibration curves confirmed the nomogram's effectiveness.

CONCLUSION: By leveraging machine learning to predict the outcomes of ICIs, we can move closer to achieving tailored ICIs for lung cancer. This advancement will assist physicians in selecting and managing subsequent treatment strategies, thereby facilitating clinical decision-making.

PMID:40181378 | DOI:10.1186/s12967-024-06057-y

Immunol Lett. 2025 Aug;274:107006. doi: 10.1016/j.imlet.2025.107006. Epub 2025 Apr 1.

ABSTRACT

OBJECTIVE: This study investigates the involvement of heparanase in IPF pathogenesis and evaluates the therapeutic potential of heparanase inhibition.

METHODS: Plasma heparanase levels were measured in IPF patients and healthy controls. Macrophage infiltration and heparanase expression in bronchoalveolar lavage fluid (BALF) were analyzed using immunofluorescence. Bleomycin (BLM)-induced pulmonary fibrosis mouse models were treated with the heparanase inhibitor OGT2115. Disease severity, macrophage polarization, and heparanase expression were assessed through histological staining, hydroxyproline content measurement, flow cytometry, immunofluorescence, Transmission Electron Microscopy and Western blot analysis.

RESULTS: Elevated heparanase levels were found in the plasma of IPF patients and in macrophages from BALF. In BLM-induced mice, heparanase was predominantly expressed in M2 macrophages. OGT2115 treatment significantly reduced mortality, body weight loss, and fibrosis severity. Additionally, OGT2115 decreased M2 macrophage infiltration, attenuated lung fibrosis, and reduced autophagy markers LC3 I/II and P62.

CONCLUSION: Heparanase plays a crucial role in modulating M2 macrophage polarization and the progression of IPF. Targeting heparanase with OGT2115 effectively ameliorates pulmonary fibrosis and represents a promising therapeutic strategy for IPF management.

PMID:40180131 | DOI:10.1016/j.imlet.2025.107006

Med Eng Phys. 2025 Apr;138:104317. doi: 10.1016/j.medengphy.2025.104317. Epub 2025 Feb 26.

ABSTRACT

Early diagnosis of breast cancer using pathological images is essential to effective treatment. With the development of deep learning techniques, breast cancer histopathology image classification methods based on neural networks develop rapidly. However, these methods usually capture features in the spatial domain, rarely consider frequency feature distributions, which limits classification performance to some extent. This paper proposes a novel breast cancer histopathology image classification network, called DWNAT-Net, which introduces Discrete Wavelet Transform (DWT) to Neighborhood Attention Transformer (NAT). DWT decomposes inputs into different frequency bands through iterative filtering and downsampling, and it can extract frequency information while retaining spatial information. NAT utilizes Neighborhood Attention (NA) to confine the attention computation to a local neighborhood around each token to enable efficient modeling of local dependencies. The proposed method was evaluated on the BreakHis and Bach datasets, yielding impressive image-level recognition accuracy rates. We achieve a recognition accuracy rate of 99.66% on the BreakHis dataset and 91.25% on the BACH dataset, demonstrating competitive performance compared to state-of-the-art methods.

PMID:40180530 | DOI:10.1016/j.medengphy.2025.104317

Med Eng Phys. 2025 Apr;138:104312. doi: 10.1016/j.medengphy.2025.104312. Epub 2025 Mar 1.

ABSTRACT

Accurate monitoring of blood glucose levels and the prediction of hyperglycemia are critical for the management of diabetes and the enhancement of medical efficiency. The primary challenge lies in uncovering the correlations among physiological information, nutritional intake, and other features, and addressing the issue of scale disparity among these features, in addition to considering the impact of individual variances on the model's accuracy. This paper introduces a universal, wearable device-assisted, multi-scale feature fusion model for real-time blood glucose monitoring and hyperglycemia prediction. It aims to more effectively capture the local correlations between diverse features and their inherent temporal relationships, overcoming the challenges of physiological data redundancy at large time scales and the incompleteness of nutritional intake data at smaller time scales. Furthermore, we have devised a personalized tuner strategy to enhance the model's accuracy and stability by continuously collecting personal data from users of the wearable devices to fine-tune the generic model, thereby accommodating individual differences and providing patients with more precise health management services. The model's performance, assessed using public datasets, indicates that the real-time monitoring error in terms of Mean Squared Error (MSE) is 0.22mmol/L, with a prediction accuracy for hyperglycemia occurrences of 96.75%. The implementation of the personalized tuner strategy yielded an average improvement rate of 1.96% on individual user datasets. This study on blood glucose monitoring and hyperglycemia prediction, facilitated by wearable devices, assists users in better managing their blood sugar levels and holds significant clinical application prospects.

PMID:40180525 | DOI:10.1016/j.medengphy.2025.104312

Toxicol Lett. 2025 Apr 1:S0378-4274(25)00057-8. doi: 10.1016/j.toxlet.2025.03.008. Online ahead of print.

ABSTRACT

Contact with specific chemicals often results in corrosive and irritative responses in the eyes and skin, playing a pivotal role in assessing the potential hazards of personal care products, cosmetics, and industrial chemicals to human health. While traditional animal testing can provide valuable information, its high costs, ethical controversies, and significant demand for animals limit its extensive use, particularly during preliminary screening stages. To address these issues, we adopted a computational modeling approach, integrating 3,316 experimental data points on eye irritation and 3,080 data points on skin irritation, to develop various machine learning and deep learning models. Under the evaluation of the external validation set, the best-performing models for the two tasks achieved balanced accuracies (BAC) of 73.0% and 75.1%, respectively. Furthermore, interpretability analyses were conducted at the dataset level, molecular level, and atomic level to provide insights into the prediction outcomes. Analysis of substructure frequencies identified structural alert fragments within the datasets. This information serves as a reference for identifying potentially irritating chemicals. Additionally, a user-friendly visualization interface was developed, enabling non-specialists to easily predict eye and skin irritation potential. In summary, our study provides a new avenue for the assessment of irritancy potential in chemicals used in pesticides, cosmetics, and ophthalmic drugs.

PMID:40180199 | DOI:10.1016/j.toxlet.2025.03.008

Brain Res Bull. 2025 Apr 1:111329. doi: 10.1016/j.brainresbull.2025.111329. Online ahead of print.

ABSTRACT

To reduce the clinical misdiagnosis rate of glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and brain metastases (BM), which are common malignant brain tumors with similar radiological features, we propose a new CNN-based model, FoTNet. The model integrates a frequency-based channel attention layer and Focal Loss to address the class imbalance issue caused by the limited data available for PCNSL. A multi-center MRI dataset was constructed by collecting and integrating data from Zhejiang University School of Medicine's Sir Run Run Shaw Hospital, along with public datasets from UPENN and TCGA. The dataset includes T1-weighted contrast-enhanced (T1-CE) MRI images from 58 GBM, 82 PCNSL, and 269 BM cases, which were divided into training and testing sets in a 5:2 ratio. FoTNet achieved a classification accuracy of 92.5% and an average AUC of 0.9754 on the test set, significantly outperforming existing machine learning and deep learning methods in distinguishing between GBM, PCNSL, and BM. Through multiple validations, FoTNet has proven to be an effective and robust tool for accurately classifying these brain tumors, providing strong support for preoperative diagnosis and assisting clinicians in making more informed treatment decisions.

PMID:40180191 | DOI:10.1016/j.brainresbull.2025.111329

Neuromolecular Med. 2025 Apr 3;27(1):24. doi: 10.1007/s12017-025-08847-z.

ABSTRACT

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that leads to motor neuron degeneration, muscle weakness, and respiratory failure. Despite ongoing research, effective treatments for ALS are limited. This study aimed to apply network biology and machine learning (ML) techniques to identify novel repurposed drug candidates for ALS. In this study, we conducted a meta-analysis using 4 transcriptome data in ALS patients (including motor neuron and muscle tissue) and healthy controls. Through this analysis, we uncovered common shared differentially expressed genes (DEGs) separately for motor neurons and muscle tissue. Using common DEGs as proxies, we identified two distinct clusters of highly clustered differential co-expressed cluster genes: the 'Muscle Tissue Cluster' for muscle tissue and the 'Motor Neuron Cluster' for motor neurons. We then evaluated the performance of the nodes of these two modules to distinguish between diseased and healthy states with ML algorithms: KNN, SVM, and Random Forest. Furthermore, we performed drug repurposing analysis and text-mining analyses, employing the nodes of clusters as drug targets to identify novel drug candidates for ALS. The potential impact of the drug candidates on the expression of cluster genes was predicted using linear regression, SVR, Random Forest, Gradient Boosting, and neural network algorithms. As a result, we identified five novel drug candidates for the treatment of ALS: Nilotinib, Trovafloxacin, Apratoxin A, Carboplatin, and Clinafloxacin. These findings highlight the potential of drug repurposing in ALS treatment and suggest that further validation through experimental studies could lead to new therapeutic avenues.

PMID:40180646 | DOI:10.1007/s12017-025-08847-z

BMJ Open. 2025 Apr 2;15(4):e088543. doi: 10.1136/bmjopen-2024-088543.

ABSTRACT

INTRODUCTION: Childhood tuberculous meningitis (TBM) is a devastating disease. The long-standing WHO recommendation for treatment is 2 months of intensive phase with isoniazid (H), rifampicin (R), pyrazinamide (Z) and ethambutol (E), followed by 10 months of isoniazid and rifampicin. In 2022, WHO released a conditional recommendation that 6 months of intensified antituberculosis therapy (ATT) could be used as an alternative for drug-susceptible TBM. However, this has never been evaluated in a randomised clinical trial. Trials evaluating ATT shortening regimens using high-dose rifampicin and drugs with better central nervous system penetration alongside adjuvant anti-inflammatory therapy are needed to improve outcomes.

METHODS AND ANALYSIS: The Shortened Intensive Therapy for Children with Tuberculous Meningitis (SURE) trial is a phase 3, randomised, partially blinded, factorial trial being conducted in Asia (India and Vietnam) and Africa (Uganda, Zambia and Zimbabwe). It is coordinated by the Medical Research Council Clinical Trial Unit at University College London (MRCCTU at UCL). 400 children (aged 29 days to <18 years) with clinically diagnosed TBM will be randomised, using a factorial design, to either a 24-week intensified regimen (isoniazid (20 mg/kg), rifampicin (30 mg/kg), pyrazinamide (40 mg/kg) and levofloxacin (20 mg/kg)) or the standard 48-week ATT regimen and 8 weeks of high-dose aspirin or placebo. The primary outcome for the first randomisation is all-cause mortality, and for the second randomisation is the paediatric modified Rankin Scale (mRS), both at 48 weeks. Nested substudies include pharmacokinetics, pharmacogenetics, pathophysiology, diagnostics and prognostic biomarkers, in-depth neurodevelopmental outcomes, MRI and health economics.

ETHICS AND DISSEMINATION: Local ethics committees at all participating study sites and respective regulators approved the SURE protocol. Ethics approval was also obtained from UCL, UK (14935/001). Informed consent from parents/carers and assent from age-appropriate children are required for all participants. Results will be published in international peer-reviewed journals, and appropriate media will be used to summarise results for patients and their families and policymakers.

TRIAL REGISTRATION: ISRCTN40829906 (registered 13 November 2018).

PMID:40180374 | DOI:10.1136/bmjopen-2024-088543

Am J Pharm Educ. 2025 Apr 1:101397. doi: 10.1016/j.ajpe.2025.101397. Online ahead of print.

ABSTRACT

OBJECTIVE: To describe the creation of a curated, shared repository of pharmacogenomics cases for pharmacy educators and lessons learned during the process.

METHODS: The 2019-2020 American Association of Colleges of Pharmacy Pharmacogenomics Special Interest Group (PGx SIG) formed an ad-hoc committee charged with creating a pharmacogenomics patient case repository for educational use. Following a needs assessment, a standardized case framework was developed using the Pharmacists' Patient Care Process. A guidance document was also created for case authors. The maintenance of the repository and the involvement of students is also described.

RESULTS: A total of 44 pharmacogenomics cases were accepted at various levels of difficulty, including 17 introductory cases, 18 intermediate cases, and 9 advanced cases. These cases cover 9 therapeutic areas and are freely available in an online network and information sharing platform (i.e., AACP Connect). Over the last 36 months, cases have been downloaded 427 times.

CONCLUSION: The AACP Pharmacogenomics SIG successfully created a shared repository of educational pharmacogenomics cases using a standardized framework. This approach can serve as a model for other Sections, SIGs or educators who desire to develop their own case repository in another field of study. Future work will measure direct outcomes of this available resource in the academic setting.

PMID:40180241 | DOI:10.1016/j.ajpe.2025.101397

EBioMedicine. 2025 Apr 2;115:105676. doi: 10.1016/j.ebiom.2025.105676. Online ahead of print.

ABSTRACT

BACKGROUND: Disruption of circadian rest-activity is prevalent in patients with bipolar disorder (BD). Lithium's impact on circadian rhythms has been documented in cell lines, animal models, and pharmacogenomics studies in patients with BD. However, the causal relationship between such disruption and BD remains unclear.

METHODS: We investigated the early effects of lithium on circadian rest-activity in an exploratory analysis of a randomised, placebo-controlled, double-blind six-week study on patients with BD. Participants were assigned to receive either lithium or a placebo in a 1:1 ratio. Circadian activity was monitored using actigraphy, and daily affect was assessed through ecological momentary assessment. A computational model was used to quantify different types of activity variability, and the impact of lithium on activity level, activity onset time and their variability were analysed using linear mixed models.

FINDINGS: Of the thirty-five participants who began treatment, 19 received lithium and 16 received a placebo. Lithium significantly altered circadian rest-activity patterns, including reducing daytime activity levels (after 4 weeks, below as well: Cohen's d = -0.19, p = 0.002, linear mixed model, ibid.), advancing the onset of daytime activity (Cohen's d = -0.14, p = 0.018), and increasing the volatility of both daytime activity level (Cohen's d = 0.10, p = 0.002) and its onset time (Cohen's d = 0.13, p < 0.001), independent of affective symptoms changes.

INTERPRETATION: This study establishes a causal link between lithium treatment and reduced circadian activity with advanced circadian phase, potentially via temporarily increasing their volatility (flexibility). Significant circadian changes were detected within one week of starting lithium, highlighting their potential as an early biomarker for treatment response.

FUNDING: This research was supported by the Wellcome Trust Strategic Award (CONBRIO: Collaborative Oxford Network for Bipolar Research to Improve Outcomes, reference No. 102,616/Z), NIHR Oxford Health Biomedical Research Centre and the NIHR Oxford cognitive health Clinical Research Facility.

PMID:40179662 | DOI:10.1016/j.ebiom.2025.105676

Eur Respir J. 2025 Apr 3;65(4):2500089. doi: 10.1183/13993003.00089-2025. Print 2025 Apr.

NO ABSTRACT

PMID:40180359 | DOI:10.1183/13993003.00089-2025

Respir Med. 2025 Apr 1:108080. doi: 10.1016/j.rmed.2025.108080. Online ahead of print.

ABSTRACT

BACKGROUND: Despite recent improvements in treatment modalities for cystic fibrosis (CF), there is currently limited evidence and a lack of consensus regarding optimal treatment strategies for the different aspects of CF, including pulmonary exacerbations (PEx). We aimed to establish a prospective cohort of people with CF (pwCF) to evaluate alternative approaches to managing CF in the era of modulator therapies.

METHODS: We prospectively enrolled children and adults with CF receiving care at specialist CF centres across Australia. Participant data were systematically collected on demography, clinical signs and symptoms, comorbidities, spirometry, participant reported outcomes, microbiology and treatments received. Here we describe the demographic, microbiological and clinical characteristics of the participants at enrolment, to understand the representativeness of the cohort for planning future nested studies.

RESULTS: Between 14 October 2020 and 31 December 2023, 927 pwCF were enrolled across eleven Australian CF centres. Of these, 51% (n=472) were male, 77% (n=709) were <18 years old, 88% (n=811) had a highest ppFEV1 (percent predicted forced expiratory volume exhaled in the first second) of ≥70% in the preceding year, and 35% (n=322) reported detection of Pseudomonas aeruginosa in their airway specimens.

CONCLUSIONS: We have established a contemporary cohort of pwCF with granular clinical and treatment data for PEx. This cohort will enable future nested studies focused on PEx management and other aspects of CF care. Understanding the baseline characteristics of these participants, as presented here, is critical for interpreting subsequent outcomes and for identifying factors that may influence disease progression and response to therapies.

PMID:40180198 | DOI:10.1016/j.rmed.2025.108080

J Psychosom Res. 2025 Mar 28;192:112118. doi: 10.1016/j.jpsychores.2025.112118. Online ahead of print.

NO ABSTRACT

PMID:40179605 | DOI:10.1016/j.jpsychores.2025.112118

Sci Rep. 2025 Apr 3;15(1):11379. doi: 10.1038/s41598-025-96052-0.

ABSTRACT

Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental health illnesses due to their subjective and complicated nature. In this work, we offer an innovative methodology for predicting mental illnesses such as epilepsy, sleep disorders, bipolar disorder, eating disorders, and depression using a multimodal deep learning framework that integrates neurocardiac data fusion. The proposed framework combines MEG, EEG, and ECG signals to create a more comprehensive understanding of brain and cardiac function in individuals with mental disorders. The multimodal deep learning approach uses an integrated CNN-Bi-Transformer, i.e., CardioNeuroFusionNet, which can process multiple types of inputs simultaneously, allowing for the fusion of various modalities and improving the performance of the predictive representation. The proposed framework has undergone testing on data from the Deep BCI Scalp Database and was further validated on the Kymata Atlas dataset to assess its generalizability. The model achieved promising results with high accuracy (98.54%) and sensitivity (97.77%) in predicting mental problems, including neurological and psychiatric conditions. The neurocardiac data fusion has been found to provide additional insights into the relationship between brain and cardiac function in neurological conditions, which could potentially lead to more accurate diagnosis and personalized treatment options. The suggested method overcomes the shortcomings of earlier studies, which tended to concentrate on single-modality data, lacked thorough neurocardiac data fusion, and made use of less advanced machine learning algorithms. The comprehensive experimental findings, which provide an average improvement in accuracy of 2.72%, demonstrate that the suggested work performs better than other cutting-edge AI techniques and generalizes effectively across diverse datasets.

PMID:40181122 | DOI:10.1038/s41598-025-96052-0