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Background This study evaluated the presentation of pulmonary sclerosing pneumocytoma (PSP) in 18F‐fluorodeoxyglucose positron emission tomography/computed tomography (18F‐FDG PET/CT) with the aim of increasing awareness of the disease. Methods Retrospective analysis was performed on 46 PSP patients who had 18F‐FDG PET/CT before surgery or pathological examination from January 2011 to December 2023. The 18F‐FDG PET/CT manifestations of PSP were summarized, and the correlation between the maximum diameter of the tumor and PET metabolic parameters was analyzed, including the maximum standardized uptake value (SUVmax), the mean SUV (SUVmean), the peak SUV (SUVpeak), metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Results The 46 tumors were randomly distributed in each lobe of both lungs. The mean maximum diameter of these lesions was 2.2 cm (range: 0.6 to 6.5 cm). The mean SUVmax was 2.96 ± 1.88 (median: 2.69, range: 0–9.02). Thirty‐three cases were categorized as mild to moderate FDG uptake, eleven cases were categorized as intense FDG uptake, and no FDG uptake was observed in the remaining two cases of the lesions qualitatively evaluated. The SUVmax of the PSP showed a positive correlation with the maximum diameter of the tumors (R = 0.493, R2 = 0.258, and p < 0.001). SUVmean (R = 0.500, R2 = 0.259, p < 0.001), SUVpeak (R = 0.553, R2 = 0.324, p < 0.001), MTV (R = 0.773, R2 = 0.592, p < 0.001) and TLG (R = 0.800, R2 = 0.654, p < 0.001) were positively correlated with the maximum diameter of the tumor. Conclusion In our study, statistically significant positive correlations were found between SUVmax, SUVmean, SUVpeak, MTV, and TLG and the maximum diameter of PSP. We found that the maximum diameter of the tumor is associated with an increase in FDG uptake in PSP, reflecting a potential correlation between lesion diameter and PET metabolic parameters, indicating a link between structural features and metabolic activity. Heatmap of correlation between the maximum diameter and PET metabolic parameters.

Abstract Aims We aimed to assess the association between gut bacterial biomarkers during early pregnancy and subsequent risk of gestational diabetes mellitus (GDM) in Chinese pregnant women. Methods Within the Tongji-Shuangliu Birth Cohort study, we conducted a nested case-control study among 201 incident GDM cases and 201 matched controls. Fecal samples were collected during early pregnancy (at 6-15 weeks), and GDM was diagnosed at 24 to 28 weeks of pregnancy. Community DNA isolated from fecal samples and V3-V4 region of 16S rRNA gene amplicon libraries were sequenced. Results In GDM cases versus controls, Rothia, Actinomyces, Bifidobacterium, Adlercreutzia, and Coriobacteriaceae and Lachnospiraceae spp. were significantly reduced, while Enterobacteriaceae, Ruminococcaceae spp., and Veillonellaceae were overrepresented. In addition, the abundance of Staphylococcus relative to Clostridium, Roseburia, and Coriobacteriaceae as reference microorganisms were positively correlated with fasting blood glucose, 1-hour and 2-hour postprandial glucose levels. Adding microbial taxa to the base GDM prediction model with conventional risk factors increased the C-statistic significantly (P < 0.001) from 0.69 to 0.75. Conclusions Gut microbiota during early pregnancy was associated with subsequent risk of GDM. Several beneficial and commensal gut microorganisms showed inverse relations with incident GDM, while opportunistic pathogenic members were related to higher risk of incident GDM and positively correlated with glucose levels on OGTT.

This study evaluated the presentation of pulmonary sclerosing pneumocytoma (PSP) in F-fluorodeoxyglucose positron emission tomography/computed tomography ( F-FDG PET/CT) with the aim of increasing awareness of the disease. Retrospective analysis was performed on 46 PSP patients who had F-FDG PET/CT before surgery or pathological examination from January 2011 to December 2023. The F-FDG PET/CT manifestations of PSP were summarized, and the correlation between the maximum diameter of the tumor and PET metabolic parameters was analyzed, including the maximum standardized uptake value (SUV ), the mean SUV (SUV ), the peak SUV (SUV ), metabolic tumor volume (MTV) and total lesion glycolysis (TLG). The 46 tumors were randomly distributed in each lobe of both lungs. The mean maximum diameter of these lesions was 2.2 cm (range: 0.6 to 6.5 cm). The mean SUV was 2.96 ± 1.88 (median: 2.69, range: 0-9.02). Thirty-three cases were categorized as mild to moderate FDG uptake, eleven cases were categorized as intense FDG uptake, and no FDG uptake was observed in the remaining two cases of the lesions qualitatively evaluated. The SUV of the PSP showed a positive correlation with the maximum diameter of the tumors (R = 0.493, R  = 0.258, and p < 0.001). SUV (R = 0.500, R  = 0.259, p < 0.001), SUV (R = 0.553, R  = 0.324, p < 0.001), MTV (R = 0.773, R  = 0.592, p < 0.001) and TLG (R = 0.800, R  = 0.654, p < 0.001) were positively correlated with the maximum diameter of the tumor. In our study, statistically significant positive correlations were found between SUV , SUV , SUV , MTV, and TLG and the maximum diameter of PSP. We found that the maximum diameter of the tumor is associated with an increase in FDG uptake in PSP, reflecting a potential correlation between lesion diameter and PET metabolic parameters, indicating a link between structural features and metabolic activity.

Problem: Recently, deep convolutional neural networks have greatly improved our ability to develop robust image analysis methods, especially those tailored to biomedical scenarios. Despite important advances in computer vision, it is usually challenging to achieve the same success on specific biomedical datasets due to certain specific challenges, such as limited size datasets, expensive annotations, high-resolution information, multiple discernible biomedical objects, and lack of interpretability. Therefore, it is still in high demand to investigate automatic image analysis methods with high interpretability and accuracy to transform high-resolution biomedical image data into meaningful quantitative information with weak annotations and limited availability of training data. Methodology: Considering the challenges of biomedical datasets and the corresponding technical limitations, this thesis aims to incorporate global context-aware information to enable automated image analysis systems to provide reliable diagnostics in clinical applications. The thesis first introduces a hybrid method equipped with U-Net and a global probabilistic model for the segmentation of dense cell populations. This model jointly exploits shape priors and global cellular interactions to compensate for the limitations of standard deep networks. Next, this thesis focuses on attention-based, interpretable fine-grained classification methods for biomedical images that is capable of jointly capturing local high-resolution detail and global background information. Outcomes: In this thesis, a number of fine-grained feature learning methods were proposed for high-resolution biomedical images. The original contributions are as follows: (1) A global probabilistic model for dense cell nucleus segmentation. (2) A gated attention mechanism for simultaneous localisation of multiple discriminative instances. (3) A multi-task learning scheme for fine-grained feature learning. (4) Two new clinical datasets for biomedical image classification.

Histopathologists establish cancer grade by assessing histological structures, such as glands in prostate cancer. Yet, digital pathology pipelines often rely on grid-based tiling that ignores tissue architecture. This introduces irrelevant information and limits interpretability. We introduce histology-informed tiling (HIT), which uses semantic segmentation to extract glands from whole slide images (WSIs) as biologically meaningful input patches for multiple-instance learning (MIL) and phenotyping. Trained on 137 samples from the ProMPT cohort, HIT achieved a gland-level Dice score of 0.83 +/- 0.17. By extracting 380,000 glands from 760 WSIs across ICGC-C and TCGA-PRAD cohorts, HIT improved MIL models AUCs by 10% for detecting copy number variation (CNVs) in genes related to epithelial-mesenchymal transitions (EMT) and MYC, and revealed 15 gland clusters, several of which were associated with cancer relapse, oncogenic mutations, and high Gleason. Therefore, HIT improved the accuracy and interpretability of MIL predictions, while streamlining computations by focussing on biologically meaningful structures during feature extraction.

Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging. Deep learning based vision systems mostly deal with high number of low-resolution images, whereas subtle detail in biomedical images require higher resolution. To bridge this gap, we propose a simple yet effective deep network that performs two tasks simultaneously in an end-to-end manner. First, it utilises a gated attention module that can focus on multiple key instances at high resolution without extra annotations or region proposals. Second, the global structural features and local instance features are fused for final image level classification. The result is a robust but lightweight end-to-end trainable deep network that yields state-of-the-art results in two separate fine-grained multi-instance biomedical image classification tasks: a benchmark breast cancer histology dataset and our new fungi species mycology dataset. In addition, we demonstrate the interpretability of the proposed model by visualising the concordance of the learned features with clinically relevant features.

Background Dopamine (DA) is a neurotransmitter that plays roles in movement, cognition, attention, and reward responses, and deficient DA signaling is associated with the progression of a number of neurological diseases, such as Parkinson’s disease. Due to its critical functions, DA expression levels in the brain are tightly controlled, with one important and rate-limiting step in its biosynthetic pathway being catalyzed by tyrosine hydroxylase (TH), an enzyme that uses iron ion (Fe 2+ ) as a cofactor. A role for metal ions has additionally been associated with the etiology of Parkinson’s disease. However, the way dopamine synthesis is regulated in vivo or whether regulation of metal ion levels is a component of DA synthesis is not fully understood. Here, we analyze the role of Catsup, the Drosophila ortholog of the mammalian zinc transporter SLC39A7 (ZIP7), in regulating dopamine levels. Results We found that Catsup is a functional zinc transporter that regulates intracellular zinc distribution between the ER/Golgi and the cytosol. Loss-of-function of Catsup leads to increased DA levels, and we showed that the increased dopamine production is due to a reduction in zinc levels in the cytosol. Zinc ion (Zn 2+ ) negatively regulates dopamine synthesis through direct inhibition of TH activity, by antagonizing Fe 2+ binding to TH, thus rendering the enzyme ineffective or non-functional. Conclusions Our findings uncovered a previously unknown mechanism underlying the control of cellular dopamine expression, with normal levels of dopamine synthesis being maintained through a balance between Fe 2+ and Zn 2+ ions. The findings also provide support for metal modulation as a possible therapeutic strategy in the treatment of Parkinson’s disease and other dopamine-related diseases.

Multimodal treatment-induced dysphagia has serious negative effects on survivors of head and neck cancer. Owing to advances in communication technologies, several studies have applied telecommunication-based interventions that incorporate swallowing exercises, education, monitoring, feedback, self-management, and communication. It is especially urgent to implement home-based remote rehabilitation in the context of the COVID-19 pandemic. However, the optimal strategy and effectiveness of remote interventions are unclear. This systematic review aimed to examine the evidence regarding the efficacy of telerehabilitation for reducing physiological and functional impairments related to swallowing and for improving adherence and related influencing factors among head and neck cancer survivors. The PubMed, MEDLINE, CINAHL, Embase, and Cochrane Library databases were systematically searched up to July 2023 to identify relevant articles. In total, 2 investigators independently extracted the data and assessed the methodological quality of the included studies using the quality assessment tool of the Joanna Briggs Institute. A total of 1465 articles were initially identified; ultimately, 13 (0.89%) were included in the systematic review. The quality assessment indicated that the included studies were of moderate to good quality. The results showed that home-based telerehabilitation improved the safety of swallowing and oral feeding, nutritional status, and swallowing-related quality of life; reduced negative emotions; improved swallowing rehabilitation adherence; was rated by participants as highly satisfactory and supportive; and was cost-effective. In addition, this review investigated factors that influenced the efficacy of telerehabilitation, which included striking a balance among swallowing training strategy, intensity, frequency, duration, and individual motor ability; treating side effects of radiotherapy; providing access to medical, motivational, and educational information; providing feedback on training; providing communication and support from speech pathologists, families, and other survivors; and addressing technical problems. Home-based telerehabilitation has shown great potential in reducing the safety risks of swallowing and oral feeding, improving quality of life and adherence, and meeting information needs for dysphagia among survivors of head and neck cancer. However, this review highlights limitations in the current literature, and the current research is in its infancy. In addition, owing to the diversity of patient sociodemographic, medical, physiological and functional swallowing, and behavioral factors, we recommend the development of tailored telemedicine interventions to achieve the best rehabilitation effects with the fewest and most precise interventions.

Herbal medicines have gained recognition among physicians and patients due to their lower adverse effects compared to modern medicines. They are extensively used to treat various diseases, including cancer, cardiovascular issues, chronic inflammation, microbial contamination, diabetes, obesity, and hepatic disorders, among others. Unfortunately, the clinical application of herbal medicines is limited by their low solubility and inadequate bioavailability. Utilizing herbal medicines in the form of nanocrystals (herbal medicine nanocrystals) has shown potential in enhancing solubility and bioavailability by reducing the particle size, increasing the specific surface area, and modifying the absorption mechanisms. Multiple studies have demonstrated that these nanocrystals significantly improve drug efficacy by reducing toxicity and increasing bioavailability. This review comprehensively examines therapeutic approaches based on herbal medicine nanocrystals. It covers the preparation principles, key factors influencing nucleation and polymorphism control, applications, and limitations. The review underscores the importance of optimizing delivery systems for successful herbal medicine nanocrystal therapeutics. Furthermore, it discusses the main challenges and opportunities in developing herbal medicine nanocrystals for the purpose of treating conditions such as cancer, inflammatory diseases, cardiovascular disorders, mental and nervous diseases, and antimicrobial infections. In conclusion, we have deliberated regarding the hurdles and forthcoming outlook in the realm of nanotoxicity, in vivo kinetics, herbal ingredients as stabilizers of nanocrystals, and the potential for surmounting drug resistance through the utilization of nanocrystalline formulations in herbal medicine. We anticipate that this review will offer innovative insights into the development of herbal medicine nanocrystals as a promising and novel therapeutic strategy.

The study of muscle disorders has gained popularity, with a particular emphasis on the relationship between adipose tissue and skeletal muscle. In our investigation, we discovered that the deletion of miR-146a-5p specifically in adipose tissue (aKO) led to a notable rise in mice’s mass and adiposity. In contrast, it led to a decline in lean mass, ability to exercise, diameter of muscle fibers, and the levels of genes associated with differentiation. The co-culture experiment showed that the transfection of miR-146a-5p mimics to 3T3-L1 significantly suppressive cell growth and promotes myotube differentiation in C2C12 cells. Exosomes from white adipose tissue (WAT) of aKO mice (aKO-WAT-Exos) significantly promoted muscle atrophy and inhibited differentiation of C2C12 cells but were reversed by co-incubation with miR-146a-5p-mimics. The miR-146a-5p can specifically target IGF-1R to improve skeletal muscle wasting. In this process, the PI3K/AKT/mTOR pathway is activated or the FoxO3 pathway is inhibited to enhance the synthesis of skeletal muscle proteins. Significantly, miR-146a-5p serves a crucial function as a microRNA in the communication of the fat-muscle connection. It can be transported through the pathway of exosomes derived from adipose tissue, ultimately ameliorating skeletal muscle atrophy and modulating body mass index (BMI). Graphical abstract