Scientific Reports最新文献

Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation of target volumes is critical. To alleviate the data demand of deep learning and promote the establishment and promotion of auto-segmentation models in small and medium-sized oncology departments and single centres, we proposed an auto-segmentation algorithm to determine the cervical cancer target volume in small samples based on multi-decoder and semi-supervised learning (MDSSL), and we evaluated the accuracy via an independent test cohort. In this study, we retrospectively collected computed tomography (CT) datasets from 71 pelvic cervical cancer patients, and a 3:4 ratio was used for the training and testing sets. The clinical target volumes (CTVs) of the primary tumour area (CTV1) and pelvic lymph drainage area (CTV2) were delineated. For definitive radiotherapy (dRT), the primary gross target volume (GTVp) was simultaneously delineated. According to the data characteristics for small samples, the MDSSL network structure based on 3D U-Net was established to train the model by combining clinical anatomical information, which was compared with other segmentation methods, including supervised learning (SL) and transfer learning (TL). The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) were used to evaluate the segmentation performance. The ability of the segmentation algorithm to improve the efficiency of online adaptive radiation therapy (ART) was assessed via geometric indicators and a subjective evaluation of radiation oncologists (ROs) in prospective clinical applications. Compared with the SL model and TL model, the proposed MDSSL model displayed the best DSC, HD95 and ASD overall, especially for the GTVp of dRT. We calculated the above geometric indicators in the range of the ground truth (head-foot direction). In the test set, the DSC, HD95 and ASD of the MDSSL model were 0.80/5.85 mm/0.95 mm for CTV1 of post-operative radiotherapy (pRT), 0.84/ 4.88 mm/0.73 mm for CTV2 of pRT, 0.84/6.58 mm/0.89 mm for GTVp of dRT, 0.85/5.36 mm/1.35 mm for CTV1 of dRT, and 0.84/4.09 mm/0.73 mm for CTV2 of dRT, respectively. In a prospective clinical study of online ART, the target volume modification time (MTime) was 3-5 min for dRT and 2-4 min for pRT, and the main duration of CTV1 modification was approximately 2 min. The introduction of the MDSSL method successfully improved the accuracy of auto-segmentation for the cervical cancer target volume in small samples, showed good consistency with RO delineation and satisfied clinical requirements. In this prospective online ART study, the application of the segmentation model was demonstrated to be useful for reducing the target volume delineation time and improving the efficiency of the online ART workflow, which can contribute to the development and promotion of cervical cancer online ART.

This study aimed to construct and assess a machine-learning algorithm designed to forecast survival rates and risk stratification for patients with gastric neuroendocrine neoplasms (gNENs) after diagnosis. Data on patients with gNENs were extracted and randomly divided into training and validation sets using the Surveillance, Epidemiology, and End Results database. We developed a prediction model using 10 machine learning algorithms across 101 combinations to forecast cancer-related mortality in patients with gNENs, selecting the best model using the highest mean over a sequence of time-dependent area under the receiver operating characteristic (ROC) curve (AUC). The performance of the final model was assessed through time-dependent ROC curves for discrimination and calibration curves for calibration. The maximum selection rank method was used to determine the best prognostic risk score threshold for classifying patients into high- and low-risk groups. Afterward, Kaplan-Meier analysis and log-rank test were used to compare survival rates among these groups. Our study examined 775 patients with gNENs, dividing them into training and validation sets. A training set comprised 543 patients, with a median follow-up of 42 months and cumulative mortality rates of 40.0% at 1 year, 48.6% at 3 years, and 54.0% at 5 years. A validation set comprised 232 patients, with cumulative mortality rates of 29.1% at 1 year, 43.5% at 3 years, and 53.2% at 5 years. The optimal random survival forest (RSF) model (mtry = 4, node size = 5) achieved an AUC of 0.839 for survival prediction in the training set. Comprising 11 variables such as demographics, treatment details, tumor characteristics, T staging, N staging, and M staging, the RSF model revealed high predictive accuracy with AUCs of 0.92, 0.96, and 0.96 for 1-, 3-, and 5-year survival, respectively, which was consistently reflected in the validation set with AUCs of 0.88, 0.92, and 0.89, respectively. Moreover, patients were risk-stratified. Although our RSF model effectively stratified patients into different prognostic groups, it needs external validation to confirm its utility for noninvasive prognostic prediction and risk stratification in gNENs. Further research is required to verify its broader clinical applicability.

Natural killer (NK) cells recognize and may kill malignant cells via their cell surface receptors. Killer cell immunoglobulin-like receptor (KIR) genotypes of donors have been reported to adjust the risk of relapse after allogeneic stem cell transplantation (HSCT), particularly in patients with acute myeloid leukemia. To test whether non-KIR NK cell receptors have a similar effect, we screened 1,638 genetic polymorphisms in 21 non-KIR NK cell receptor genes for their associations with relapse and graft-versus-host disease (GVHD) after HSCT in 1,491 HSCT donors (from Finland, the UK, Spain, and Poland), divided into a discovery and replication cohort. Eleven polymorphisms regulating or located in CD226, CD244, FCGR3A, KLRD1, NCR3, and PVRIG were associated with the risks for relapse and GVHD. These associations could not be confirmed in the replication cohort. Blood donor NK cells carrying alleles showing genetic protection for relapse had a higher in vitro NK cell killing activity than non-carriers whereas those with alleles genetically protective for GVHD had lower cytotoxicity, potentially indicating functional effects. Taken together, these results show no robust effects of genetic variation in the tested non-KIR NK cell receptors on the outcome of HSCT.

Decoding locomotor tasks is crucial in cognitive neuroscience for understanding brain responses to physical tasks. Traditional methods like EEG offer brain activity insights but may require additional modalities for enhanced interpretative precision and depth. The integration of EEG with ocular metrics, particularly eye blinks, presents a promising avenue for understanding cognitive processes by combining neural and ocular behaviors. However, synchronizing EEG and eye blink activities poses a significant challenge due to their frequently inconsistent alignment. Our study with 35 participants performing various locomotor tasks such as standing, walking, and transversing obstacles introduced a novel methodology, pcEEG+, which fuses EEG principal components (pcEEG) with aligned eye blink data (syncBlink). The results demonstrated that pcEEG+ significantly improved decoding accuracy in locomotor tasks, reaching 78% in some conditions, and surpassed standalone pcEEG and syncBlink methods by 7.6% and 22.7%, respectively. The temporal generalization matrix confirmed the consistency of pcEEG+ across tasks and times. The results were replicated using two driving simulator datasets, thereby confirming the validity of our method. This study demonstrates the efficacy of the pcEEG+ method in decoding locomotor tasks, underscoring the importance of temporal synchronization for accuracy and offering a deeper insight into brain activity during complex movements.

Human bocavirus (HBoV) is an emerging pathogen causing gastroenteritis/respiratory tract infection. Shellfish has been implicated in foodborne HBoV dissemination. The present investigation aimed at synthesising shellfish-associated HBoV data. Shellfish-HBoV data were mined from public repositories using topic-specific algorithm. A total of 30 data sources was identified of which 5 were synthesised. The average HBoV positivity and sample-size was 12 ± 9.2 and 134.2 ± 113.6, respectively. HBoV was studied in mollusc with 3.7-83.3% crude prevalence. The pooled HBoV prevalence in shellfish was 9.2% (7.2-11.8; 5 studies) and 12.9% (1.8-53.9; 5 studies) in common-effects and random-effects model respectively, with 0.12-94.89% prediction interval (PI). Sensitivity analysis yielded 8.7% (6.7-11.2; PI = 1.99-29.48%) prevalence. HBoV1 and HBoV2 pooled prevalence in shellfish was 7.91% (1.61-31.09; 3 studies) and 12.52% (0.01-99.60; 3 studies), respectively. HBoV3 prevalence was reported in one single study as 6.96% (4.41-10.35). In conclusion, the present study revealed high HBoV prevalence in shellfish, signifying the need to characterise HBoV and subtypes circulating in non-mollusc shellfish. Furthermore, there is an urgent need to mitigate the food safety risk that may result from HBoV contaminated shellfish since shellfish-borne HBoV is not routinely assessed and might be underestimated at present.

Despite the end of the global Coronavirus Disease 2019 (COVID-19) pandemic, the risk factors for COVID-19 severity continue to be a pivotal area of research. Specifically, studying the impact of the genomic diversity of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) on COVID-19 severity is crucial for predicting severe outcomes. Therefore, this study aimed to investigate the impact of the SARS-CoV-2 genome sequence, genotype, patient age, gender, and vaccination status on the severity of COVID-19, and to develop accurate and robust prediction models. The training set (n = 12,038), primary testing set (n = 4,006), and secondary testing set (n = 2,845) consist of SARS-CoV-2 genome sequences with patient information, which were obtained from Global Initiative on Sharing all Individual Data (GISAID) spanning over four years. Four machine learning methods were employed to construct prediction models. By extracting SARS-CoV-2 genomic features, optimizing model parameters, and integrating models, this study improved the prediction accuracy. Furthermore, Shapley Additive exPlanes (SHAP) was applied to analyze the interpretability of the model and to identify risk factors, providing insights for the management of severe cases. The proposed ensemble model achieved an F-score of 88.842% and an Area Under the Curve (AUC) of 0.956 on the global testing dataset. In addition to factors such as patient age, gender, and vaccination status, over 40 amino acid site mutation characteristics were identified to have a significant impact on the severity of COVID-19. This work has the potential to facilitate the early identification of COVID-19 patients with high risks of severe illness, thus effectively reducing the rates of severe cases and mortality.

Breast cancer (BC) is the leading cause of death among cancer patients worldwide. In 2020, almost 12% of all cancers were diagnosed with BC. Therefore, it is important to search for new potential markers of cancer progression that could be helpful in cancer diagnostics and successful anti-cancer therapies. In this study, we investigated the potential role of the lysine acetyltransferases KAT6A and KAT6B in the outcome of patients with invasive breast carcinoma. The expression profiles of KAT6A/B in 495 cases of IDC and 38 cases of mastopathy (FBD) were examined by immunohistochemistry. KAT6A/B expression was also determined in the breast cancer cell lines MCF-7, BT-474, SK-BR-3, T47D, MDA-MB-231, and MDA-MB-231/BO2, as well as in the human epithelial mammary gland cell line hTERT-HME1 - ME16C, both at the mRNA and protein level. Statistical analysis of the results showed that the nuclear expression of KAT6A/B correlates with the estrogen receptor status: KAT6A_NUC vs. ER r = 0.2373 and KAT6B_NUC vs. ER r = 0.1496. Statistical analysis clearly showed that KAT6A cytoplasmic and nuclear expression levels were significantly higher in IDC samples than in FBD samples (IRS 5.297 ± 2.884 vs. 2.004 ± 1.072, p < 0.0001; IRS 5.133 ± 4.221 vs. 0.1665 ± 0.4024, p < 0.0001, respectively). Moreover, we noticed strong correlations between ER and PR status and the nuclear expression of KAT6A and KAT6B (nucKAT6A vs. ER, p = 0.0048; nucKAT6A vs. PR p = 0.0416; nucKAT6B vs. ER p = 0.0306; nucKAT6B vs. PR p = 0.0213). Significantly higher KAT6A and KAT6B expression was found in the ER-positive cell lines T-47D and BT-474, whereas significantly lower expression was observed in the triple-negative cell lines MDA-MB-231 and MDA-MB-231/BO2. The outcomes of small interfering RNA (siRNA)-mediated suppression of KAT6A/B genes revealed that within estrogen receptor (ER) positive and negative cell lines, MCF-7 and MDA-MB-231, attenuation of KAT6A led to concurrent attenuation of KAT6A, whereas suppression of KAT6B resulted in simultaneous attenuation of KAT6A. Furthermore, inhibition of KAT6A/B genes resulted in a reduction in estrogen receptor (ER) mRNA and protein expression levels in MCF-7 and MDA-MMB-231 cell lines. Based on our findings, the lysine acetyltransferases KAT6A and KAT6B may be involved in the progression of invasive ductal breast cancer. Further research on other types of cancer may show that KAT6A and KAT6B could serve as diagnostic and prognostic markers for these types of malignancies.

Metal matrix composites (MMCs) are lightweight and widely used materials constantly applied in various industries. Such material's structural and functional properties change under the contributions of various reinforcing particles and base materials. Multiple technologies are used in the manufacturing and machining these materials, and numerous studies are oriented toward this domain through academic and industrial projects. One aspect that receives less attention is understanding the combined effects of cutting parameters, lubrication conditions, and reinforcing elements on the machinability of such materials. Amongst MMC, limited attention has been paid to A520 alloys reinforced with SiC particles. Therefore, this work investigated the tool wear size and morphology in milling A520-10%SiC under various lubrication and cutting conditions. It was observed that cutting conditions significantly affect the tool life and wear morphology when machining A520-10%SiC. The main wear modes observed were abrasion and adhesion, mainly presented as the built-up edge (BUE) and Built-up layer (BUL). The wet method reduced the formation of BUE and BUL by 95% and MQL by 60% compared to the dry method. It was also observed that better tool life was observed under wet mode than readings made under MQL and dry modes. The outcomes could generate a practical window for the optimum selection of cutting parameters when machining reinforced Al-MMCs, in principle, A520-10%SiC.

Cannabinoid receptor activation has been proposed to trigger glutamate release from astrocytes located in cortical layer 2/3. Here, we measure the basal concentration of extracellular glutamate in layer 2/3 of mouse somatosensory cortex and find it to be 20-30 nM. We further examine the effect of cannabinoid receptor signaling on extracellular glutamate, and find no evidence for increased extracellular glutamate upon cannabinoid receptor agonist application.