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Cancer Medicine Jun 2024Sarcopenia is highly prevalent among patients with colorectal cancer (CRC). Computed tomography (CT)-based assessment of low skeletal muscle index (SMI) is widely used...
BACKGROUND
Sarcopenia is highly prevalent among patients with colorectal cancer (CRC). Computed tomography (CT)-based assessment of low skeletal muscle index (SMI) is widely used for diagnosing sarcopenia. However, there are conflicting findings on the association between low SMI and overall survival (OS) in CRC patients. The objective of this study was to investigate whether CT-determined low SMI can serve as a valuable prognostic factor in CRC.
METHODS
We collected data from patients with CRC who underwent radical surgery at our institution between June 2020 and November 2021. The SMI at the third lumbar vertebra was calculated using CT scans, and the cutoff values for defining low SMI were determined using receiver operating characteristic curves. Univariate and multivariate analyses were performed to assess the associations between clinical characteristics and postoperative major complications.
RESULTS
A total of 464 patients were included in the study, 229 patients (46.7%) were classified as having low SMI. Patients with low SMI were older and had a lower body mass index (BMI), a higher neutrophil to lymphocyte ratio (NLR), and higher nutritional risk screening 2002 (NRS2002) scores compared to those with normal SMI. Furthermore, patients with sarcopenia had a higher rate of major complications (10.9% vs. 1.3%; p < 0.001) and longer length of stay (9.09 ± 4.86 days vs. 8.25 ± 3.12 days; p = 0.03). Low SMI and coronary heart disease were identified as independent risk factors for postoperative major complications. Moreover, CRC patients with low SMI had significantly worse OS. Furthermore, the combination of low SMI with older age or TNM stage II + III resulted in the worst OS in each subgroup analysis.
CONCLUSIONS
CT-determined low SMI is associated with poor prognosis in patients with CRC, especially when combined with older age or advanced TNM stage.
Topics: Humans; Male; Female; Colorectal Neoplasms; Sarcopenia; Aged; Tomography, X-Ray Computed; Prognosis; Middle Aged; Muscle, Skeletal; Postoperative Complications; Retrospective Studies; Body Mass Index; ROC Curve
PubMed: 38924332
DOI: 10.1002/cam4.7328 -
Proceedings of the National Academy of... Jul 2024The spatial distribution of proteins and their arrangement within the cellular ultrastructure regulates the opening of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic...
The spatial distribution of proteins and their arrangement within the cellular ultrastructure regulates the opening of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in response to glutamate release at the synapse. Fluorescence microscopy imaging revealed that the postsynaptic density (PSD) and scaffolding proteins in the presynaptic active zone (AZ) align across the synapse to form a trans-synaptic "nanocolumn," but the relation to synaptic vesicle release sites is uncertain. Here, we employ focused-ion beam (FIB) milling and cryoelectron tomography to image synapses under near-native conditions. Improved image contrast, enabled by FIB milling, allows simultaneous visualization of supramolecular nanoclusters within the AZ and PSD and synaptic vesicles. Surprisingly, membrane-proximal synaptic vesicles, which fuse to release glutamate, are not preferentially aligned with AZ or PSD nanoclusters. These synaptic vesicles are linked to the membrane by peripheral protein densities, often consistent in size and shape with Munc13, as well as globular densities bridging the synaptic vesicle and plasma membrane, consistent with prefusion complexes of SNAREs, synaptotagmins, and complexin. Monte Carlo simulations of synaptic transmission events using biorealistic models guided by our tomograms predict that clustering AMPARs within PSD nanoclusters increases the variability of the postsynaptic response but not its average amplitude. Together, our data support a model in which synaptic strength is tuned at the level of single vesicles by the spatial relationship between scaffolding nanoclusters and single synaptic vesicle fusion sites.
Topics: Synaptic Vesicles; Electron Microscope Tomography; Animals; Rats; Post-Synaptic Density; Cryoelectron Microscopy; Synapses
PubMed: 38923992
DOI: 10.1073/pnas.2403136121 -
Cancer Medicine Jun 2024Accurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the...
BACKGROUND
Accurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions can be challenging. Recent studies have demonstrated how machine learning techniques can be applied to clinical data to solve this diagnostic problem. However, ML models can often consider as black-boxes due to the difficulty of understanding the decision-making process used by the algorithm to obtain a specific result.
AIMS
For this purpose, we propose an Explainable Artificial Intelligence model trained on clinical characteristics and qualitative ultrasound indicators to predict solid adnexal masses diagnosis.
MATERIALS & METHODS
Since the diagnostic task was a three-class problem (benign tumor, invasive cancer, or ovarian metastasis), we proposed a waterfall classification model: a first model was trained and validated to discriminate benign versus malignant, a second model was trained to distinguish nonmetastatic versus metastatic malignant lesion which occurs when a patient is predicted to be malignant by the first model. Firstly, a stepwise feature selection procedure was implemented. The classification performances were validated on Leave One Out scheme.
RESULTS
The accuracy of the three-class model reaches an overall accuracy of 86.36%, and the precision per-class of the benign, nonmetastatic malignant, and metastatic malignant classes were 86.96%, 87.27%, and 77.78%, respectively.
DISCUSSION
SHapley Additive exPlanations were performed to visually show how the machine learning model made a specific decision. For each patient, the SHAP values expressed how each characteristic contributed to the classification result. Such information represents an added value for the clinical usability of a diagnostic system.
CONCLUSIONS
This is the first work that attempts to design an explainable machine-learning tool for the histological diagnosis of solid masses of the ovary.
Topics: Humans; Female; Machine Learning; Ultrasonography; Ovarian Neoplasms; Middle Aged; Adult; Adnexal Diseases; Aged; Algorithms; Diagnosis, Differential
PubMed: 38923847
DOI: 10.1002/cam4.7425 -
The Kaohsiung Journal of Medical... Jun 2024Thyroid dyshormonogenesis (TDH) is responsible for 15%-25% of congenital hypothyroidism (CH) cases. Pathogenetic variants of this common inherited endocrine disorders...
Thyroid dyshormonogenesis (TDH) is responsible for 15%-25% of congenital hypothyroidism (CH) cases. Pathogenetic variants of this common inherited endocrine disorders vary geographically. Unraveling the genetic underpinnings of TDH is essential for genetic counseling and precise therapeutic strategies. This study aims to identify genetic variants associated with TDH in Southern Taiwan using whole exome sequencing (WES). We included CH patients diagnosed through newborn screening at a tertiary medical center from 2011 to 2022. Permanent TDH was determined based on imaging evidence of bilateral thyroid structure and the requirement for continuous medication beyond 3 years of age. Genomic DNA extracted from blood was used for exome library construction, and pathogenic variants were detected using an in-house algorithm. Of the 876 CH patients reviewed, 121 were classified as permanent, with 47 (40%) confirmed as TDH. WES was conducted for 45 patients, and causative variants were identified in 32 patients (71.1%), including DUOX2 (15 cases), TG (8 cases), TSHR (7 cases), TPO (5 cases), and DUOXA2 (1 case). Recurrent variants included DUOX2 c.3329G>A, TSHR c.1349G>A, TG c.1348delT, and TPO c.2268dupT. We identified four novel variants based on genotype, including TSHR c.1135C>T, TSHR c.1349G>C, TG c.2461delA, and TG c.2459T>A. This study underscores the efficacy of WES in providing definitive molecular diagnoses for TDH. Molecular diagnoses are instrumental in genetic counseling, formulating treatment, and developing management strategies. Future research integrating larger population cohorts is vital to further elucidate the genetic landscape of TDH.
PubMed: 38923290
DOI: 10.1002/kjm2.12871 -
Journal of Cellular and Molecular... Jun 2024Studies have reported variable effects of sex hormones on serious diseases. Severe disease and mortality rates in COVID-19 show marked gender differences that may be... (Review)
Review
Studies have reported variable effects of sex hormones on serious diseases. Severe disease and mortality rates in COVID-19 show marked gender differences that may be related to sex hormones. Sex hormones regulate the expression of the viral receptors ACE2 and TMPRSS2, which affect the extent of viral infection and consequently cause variable outcomes. In addition, sex hormones have complex regulatory mechanisms that affect the immune response to viruses. These hormones also affect metabolism, leading to visceral obesity and severe disease can result from complications such as thrombosis. This review presents the latest researches on the regulatory functions of hormones in viral receptors, immune responses, complications as well as their role in COVID-19 progression. It also discusses the therapeutic possibilities of these hormones by reviewing the recent findings of clinical and assay studies.
Topics: Humans; COVID-19; Gonadal Steroid Hormones; Angiotensin-Converting Enzyme 2; SARS-CoV-2; Serine Endopeptidases; Female; Severity of Illness Index; Male
PubMed: 38923119
DOI: 10.1111/jcmm.18490 -
Nanomaterials (Basel, Switzerland) Jun 2024Magnetic particle hyperthermia (MPH) enables the direct heating of solid tumors with alternating magnetic fields (AMFs). One challenge with MPH is the unknown particle...
Magnetic particle hyperthermia (MPH) enables the direct heating of solid tumors with alternating magnetic fields (AMFs). One challenge with MPH is the unknown particle distribution in tissue after injection. Magnetic particle imaging (MPI) can measure the nanoparticle content and distribution in tissue after delivery. The objective of this study was to develop a clinically translatable protocol that incorporates MPI data into finite element calculations for simulating tissue temperatures during MPH. To verify the protocol, we conducted MPH experiments in tumor-bearing mouse cadavers. Five 8-10-week-old female BALB/c mice bearing subcutaneous 4T1 tumors were anesthetized and received intratumor injections of Synomag-S90 nanoparticles. Immediately following injection, the mice were euthanized and imaged, and the tumors were heated with an AMF. We used the Mimics Innovation Suite to create a 3D mesh of the tumor from micro-computerized tomography data and spatial index MPI to generate a scaled heating function for the heat transfer calculations. The processed imaging data were incorporated into a finite element solver, COMSOL Multiphysics. The upper and lower bounds of the simulated tumor temperatures for all five cadavers demonstrated agreement with the experimental temperature measurements, thus verifying the protocol. These results demonstrate the utility of MPI to guide predictive thermal calculations for MPH treatment planning.
PubMed: 38921935
DOI: 10.3390/nano14121059 -
Journal of Imaging May 2024X-ray Fluorescence Computed Tomography (XFCT) is an emerging non-invasive imaging technique providing high-resolution molecular-level data. However, increased...
X-ray Fluorescence Computed Tomography (XFCT) is an emerging non-invasive imaging technique providing high-resolution molecular-level data. However, increased sensitivity with current benchtop X-ray sources comes at the cost of high radiation exposure. Artificial Intelligence (AI), particularly deep learning (DL), has revolutionized medical imaging by delivering high-quality images in the presence of noise. In XFCT, traditional methods rely on complex algorithms for background noise reduction, but AI holds promise in addressing high-dose concerns. We present an optimized Swin-Conv-UNet (SCUNet) model for background noise reduction in X-ray fluorescence (XRF) images at low tracer concentrations. Our method's effectiveness is evaluated against higher-dose images, while various denoising techniques exist for X-ray and computed tomography (CT) techniques, only a few address XFCT. The DL model is trained and assessed using augmented data, focusing on background noise reduction. Image quality is measured using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), comparing outcomes with 100% X-ray-dose images. Results demonstrate that the proposed algorithm yields high-quality images from low-dose inputs, with maximum PSNR of 39.05 and SSIM of 0.86. The model outperforms block-matching and 3D filtering (BM3D), block-matching and 4D filtering (BM4D), non-local means (NLM), denoising convolutional neural network (DnCNN), and SCUNet in both visual inspection and quantitative analysis, particularly in high-noise scenarios. This indicates the potential of AI, specifically the SCUNet model, in significantly improving XFCT imaging by mitigating the trade-off between sensitivity and radiation exposure.
PubMed: 38921604
DOI: 10.3390/jimaging10060127 -
Metabolites Jun 2024Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic...
Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic alterations. While multiparametric imaging enhances the characterization of ITH by capturing both spatial and functional variations, it falls short in directly assessing the metabolic activities that underpin these phenotypic differences. This gap stems from the challenge of integrating easily accessible, colocated pathology and detailed genomic data with metabolic insights. This study presents a multifaceted approach combining stereotactic biopsy with standard clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. This work aims to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma. Each patient underwent a multiparametric MR scan (T1, T1, T2, T2-FLAIR, DWI) prior to surgery. During standard craniotomy, at least 1 stereotactic biopsy was collected from each patient, with screenshots of the sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard receiver operating characteristic (ROC) analyses were used to evaluate detection, with AUC (area under curve) calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75 ± 0.11; individual AUCs were as high as 0.96 for both IDH1 and TP53 with T2-FLAIR and ADC, and 0.99 for EGFR with T2 and ADC. These results suggest the possibility of predicting exome-wide mutation events from noninvasive, in vivo imaging by combining stereotactic localization of glioma samples and a semi-parametric deep learning method. The genomic alterations identified, particularly in IDH1, TP53, EGFR, PIK3CA, and NF1, are known to play pivotal roles in metabolic pathways driving glioma heterogeneity. Our methodology, therefore, indirectly sheds light on the metabolic landscape of glioma through the lens of these critical genomic markers, suggesting a complex interplay between tumor genomics and metabolism. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.
PubMed: 38921472
DOI: 10.3390/metabo14060337 -
Metabolites May 2024A multimodal mass spectrometry imaging (MSI) approach was used to investigate the chemotherapy drug-induced response of a Multicellular Tumour Spheroid (MCTS) 3D cell...
A multimodal mass spectrometry imaging (MSI) approach was used to investigate the chemotherapy drug-induced response of a Multicellular Tumour Spheroid (MCTS) 3D cell culture model of osteosarcoma (OS). The work addresses the critical demand for enhanced translatable early drug discovery approaches by demonstrating a robust spatially resolved molecular distribution analysis in tumour models following chemotherapeutic intervention. Advanced high-resolution techniques were employed, including desorption electrospray ionisation (DESI) mass spectrometry imaging (MSI), to assess the interplay between metabolic and cellular pathways in response to chemotherapeutic intervention. Endogenous metabolite distributions of the human OS tumour models were complemented with subcellularly resolved protein localisation by the detection of metal-tagged antibodies using Imaging Mass Cytometry (IMC). The first application of matrix-assisted laser desorption ionization-immunohistochemistry (MALDI-IHC) of 3D cell culture models is reported here. Protein localisation and expression following an acute dosage of the chemotherapy drug doxorubicin demonstrated novel indications for mechanisms of region-specific tumour survival and cell-cycle-specific drug-induced responses. Previously unknown doxorubicin-induced metabolite upregulation was revealed by DESI-MSI of MCTSs, which may be used to inform mechanisms of chemotherapeutic resistance. The demonstration of specific tumour survival mechanisms that are characteristic of those reported for in vivo tumours has underscored the increasing value of this approach as a tool to investigate drug resistance.
PubMed: 38921450
DOI: 10.3390/metabo14060315 -
Current Oncology (Toronto, Ont.) Jun 2024Small cell bladder cancer (SCBC) is a rare and aggressive disease, often treated with platinum/etoposide-based chemotherapy. Key molecular drivers include the...
Small cell bladder cancer (SCBC) is a rare and aggressive disease, often treated with platinum/etoposide-based chemotherapy. Key molecular drivers include the inactivation of onco-suppressor genes (, ) and amplifications in proto-oncogenes (). We report a patient with SCBC who achieved an objective and prolonged response to lurbinectedin, which has been approved for metastatic small cell lung cancer, after developing disease progression on cisplatin/etoposide and nivolumab/ipilimumab. A genomic analysis of a metastatic biopsy prior to lurbinectedin initiation revealed a mutation and amplification of the cell cycle regulators and . A repeat biopsy following the development of lurbinectedin resistance showed a new actionable ERBB2 alteration without significant change in the tumor mutation burden (six mutations/Mb). The present report suggests that lurbinectedin may be active and should be further explored in SCBC harboring mutations and amplifications in E2F3 and MYC family complexes.
Topics: Humans; Carbolines; Urinary Bladder Neoplasms; Heterocyclic Compounds, 4 or More Rings; Mutation; Tumor Suppressor Protein p53; Male; Carcinoma, Small Cell; Heterocyclic Compounds, 3-Ring; Antineoplastic Agents; Middle Aged
PubMed: 38920737
DOI: 10.3390/curroncol31060254