The majority of patients with thyroid cancer can attain a favorable prognosis with a comprehensive treatment program based on surgical treatment. However, the current treatment options for advanced thyroid cancer are still limited. In recent years, chimeric antigen receptor-modified T-cell (CAR-T) therapy has received widespread attention in the field of oncology treatment. It has achieved remarkable results in the treatment of hematologic tumors. However, due to the constraints of multiple factors, the therapeutic efficacy of CAR-T therapy for solid tumors, including thyroid cancer, has not yet met expectations. This review outlines the fundamental structure and treatment strategies of CAR-T cells, provides an overview of the advancements in both preclinical investigations and clinical trials focusing on targets associated with CAR-T cell therapy in treating thyroid cancer, and discusses the challenges and solutions to CAR-T cell therapy for thyroid cancer. In conclusion, CAR-T cell therapy is a promising therapeutic approach for thyroid cancer, and we hope that our review will provide a timely and updated study of CAR-T cell therapy for thyroid cancer to advance the field.

Objective.Medical imaging offered a non-invasive window to visualize tumors, with radiomics transforming these images into quantitative data for tumor phenotyping. However, the intricate web linking imaging features, clinical endpoints, and tumor biology was mostly uncharted. This study aimed to unravel the connections between CT imaging features and clinical characteristics, including tumor histopathological grading, clinical stage, and endocrine symptoms, alongside immunohistochemical markers of tumor cell growth, such as the Ki-67 index and nuclear mitosis rate.Approach.We conducted a retrospective analysis of data from 137 patients with pancreatic neuroendocrine tumors who had undergone contrast-enhanced CT scans across two institutions. Our study focused on three clinical factors: pathological grade, clinical stage, and endocrine symptom status, in addition to two immunohistochemical markers: the Ki-67 index and the rate of nuclear mitosis. We computed both predefined (2D and 3D) and learning-based features (via sparse autoencoder, or SAE) from the scans. To unearth the relationships between imaging features, clinical factors, and immunohistochemical markers, we employed the Spearman rank correlation along with the Benjamini-Hochberg method. Furthermore, we developed and validated radiomics signatures to foresee these clinical factors.Main results.The 3D imaging features showed the strongest relationships with clinical factors and immunohistochemical markers. For the association with pathological grade, the mean absolute value of the correlation coefficient (CC) of 2D, SAE, and 3D features was 0.3318 ± 0.1196, 0.2149 ± 0.0361, and 0.4189 ± 0.0882, respectively. While for the association with Ki-67 index and rate of nuclear mitosis, the 3D features also showed higher correlations, with CC as 0.4053 ± 0.0786 and 0.4061 ± 0.0806. In addition, the 3D feature-based signatures showed optimal performance in clinical factor prediction.Significance.We found relationships between imaging features, clinical factors, and immunohistochemical markers. The 3D features showed higher relationships with clinical factors and immunohistochemical markers.

Sugarcane, a significant cash crop in tropical and subtropical regions, contributes to 80% of sugar production and 40% of bioethanol production in the world. It is a key sugar crop, accounting for 85% of sugar production in China. Developing new varieties with high yield, high sugar, and better stress resistance is crucial for the sustainable growth of sugar industry. Hybrid breeding is the most widely used and effective method, with over 98% of Chinese sugarcane varieties resulting from this approach. Over the past two decades, Chinese breeders have developed the theory of high-heterogeneous composite high-sugar breeding, leading to the successful breeding of the fifth-generation sugarcane varieties. Among them, YZ08-1609, a complex hybrid of Saccharum spp., was developed by Sugarcane Research Institute (YSRI) of Yunnan Academy of Agricultural Sciences. The average cane yield of YZ08-1609 was 14.4% higher than ROC22. It is highly resistant to mosaic disease, and highly tolerant to drought stress, but moderately susceptible to smut disease. Notably, YZ08-1609 stands out with a sucrose content of 20.3%, setting an international record, earning the reputation as \"King of Sugar\". To summarize experience and inspire breeding, we provided here the detailed insights into the selection of parents, breeding process, and characteristics of YZ08-1609. Besides, the biological mechanisms underlying its high yield and high sugar was excavated at both transcriptional and metabolic levels. The challenges and prospects in breeding sugarcane varieties especially with high sugar were also discussed, offering a foundation for the future development of high-sugar varieties.

Gastrointestinal cancers account for 11.6 % of all cancers, and are the second most frequently diagnosed type of cancer worldwide. Traditional Chinese medicine (TCM), together with Western medicine or alone, has unique advantages for the prevention and treatment of cancers, including gastrointestinal cancers. Syndrome differentiation and treatment are basic characteristics of the theoretical system of TCM. TCM syndromes are the result of the differentiation of the syndrome and the basis of treatment. Genomics, transcriptomics, proteomics, metabolomics, intestinal microbiota, and serology, generated around the central law, are used to study the biological basis of TCM syndromes in gastrointestinal cancers. This review summarizes current research on the biological basis of TCM syndrome in gastrointestinal cancers and provides useful references for future research on TCM syndrome in gastrointestinal cancers.

Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience.
To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis.
Retrospective.
1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets.
3.0T/dynamic contrast-enhanced (DCE)-MRI sequence.
DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists\' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data.
Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value.
The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation.
The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis.
4 TECHNICAL EFFICACY STAGE: 3.

Hippocampal morphological change is one of the main hallmarks of Alzheimer\'s disease (AD). However, whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment (MCI) to AD dementia and whether these features provide any neurobiological foundation remains unclear. The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging (MRI) markers for AD. Multivariate classifier-based support vector machine (SVM) analysis provided individual-level predictions for distinguishing AD patients (n = 261) from normal controls (NCs; n = 231) with an accuracy of 88.21% and intersite cross-validation. Further analyses of a large, independent the Alzheimer\'s Disease Neuroimaging Initiative (ADNI) dataset (n = 1228) reinforced these findings. In MCI groups, a systemic analysis demonstrated that the identified features were significantly associated with clinical features (e.g., apolipoprotein E (APOE) genotype, polygenic risk scores, cerebrospinal fluid (CSF) Aβ, CSF Tau), and longitudinal changes in cognition ability; more importantly, the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up. These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI, and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus. The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI.

The objectives of this study were: (1) to identify and rank phenotypically divergent animals for residual feed intake (RFI) regarding their efficiency (high: HE or low: LE); (2) to evaluate their relationships with ingestive behavior, ruminal and blood parameters, performance, and infrared thermography; and (3) to determine if such measurements can be used as feed efficiency markers in rearing dairy heifers. Thirty-eight heifers, 143 d ± 4 (Mean ± SD) of age and 108.7 kg ± 17.9 of body weight were used. The animals were fed with a total mixed ration during the 91 d of the trial. A phenotypic divergence of DMI for RFI was observed between -0.358 and 0.337 kg/d for HE and LE, respectively. Dry matter intake (DMI) was lower in the HE (2.5 kg DMI/d vs. 3.1 kg DMI/d), as was the number of visits to the feed bin with consumption (59 vs. 71). Feed intake was the best predictor of said divergence. Water intake and number of visits to the feed bin were presented moderate correlations with RFI. The ruminal fermentation variables, blood metabolites, blood hormones (such as the other ingestive behavior variables), and infrared thermography were not able to accurately predict HE or LE animals.

A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression (R = 0.29, p < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI.

The neuropeptide oxytocin is released both into the blood and within the brain in response to reproductive stimuli, such as birth, suckling and sex, but also in response to social interaction and stressors. Substance use disorders, or addictions, are chronic, relapsing brain disorders and are one of the major causes of global burden of disease. Unfortunately, current treatment options for substance use disorders are extremely limited and a treatment breakthrough is sorely needed. There is mounting preclinical evidence that targeting the brain oxytocin system may provide that breakthrough. Substance use disorders are characterised by a viscous cycle of bingeing and intoxication, followed by withdrawal and negative affect, and finally preoccupation and anticipation that triggers relapse and further consumption. Administration of oxytocin has been shown to have a potential therapeutic benefit at each stage of this addiction cycle for numerous drugs of abuse. This multidimensional therapeutic utility is likely due to oxytocin\'s interactions with key biological systems that underlie the development and maintenance of addiction. Only a few human trials of oxytocin in addicted populations have been completed with the results thus far being mixed. There are numerous other trials underway, and the results are eagerly awaited. However, the ability to fully harness the potential therapeutic benefit of targeting the brain oxytocin system may depend on the development of molecules that selectively stimulate the oxytocin system, but that have superior pharmacokinetic properties to oxytocin itself.