The role of the thyroid in polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is the most common endocrine and metabolic disease in women of childbearing age and can cause metabolic disorder, infertility, and increased anxiety and depression; as a result, it can seriously affect the physical and mental health of fertile women. PCOS is a highly clinically heterogeneous disease with unclear etiology and pathogenesis, which increases the difficulty of treatment. The thyroid gland has complex regulatory effects on metabolism, reproduction, and emotion, and produces hormones that act on almost all cells of the human body. The clinical manifestations of PCOS are similar to some thyroid diseases. Furthermore, some thyroid diseases, such as subclinical hypothyroidism (SCH), not only increase the incidence rate of PCOS, but also exacerbate its associated metabolic abnormalities and reproductive disorders. Interestingly, PCOS also increases the incidence of some thyroid diseases. However, the role of the thyroid in PCOS remains unclear. This review is intended to thoroughly explore the critical role of the thyroid in PCOS by summarizing the comorbidity of PCOS and thyroid diseases and their combined role in metabolic disorders, related metabolic diseases, and reproductive disorders; and by analyzing the potential mechanism through which the thyroid influences the development and progression of PCOS and its symptoms. We hope this review will provide a valuable reference for the role of the thyroid in PCOS

Quantum efficiency measurement of single photon detectors using photon pairs generated in optical fibers

Using the correlated signal and idler photon pairs generated in a dispersion shifted fiber by a pulsed pump, we measure the quantum efficiency of a InGaAs/InP avalanche photodiode-based single photon detector. Since the collection efficiency of photon pairs is a key parameter to correctly deduce the quantum efficiency, we carefully characterize the collection efficiency by studying correlation dependence of photon pairs upon the spectra of pump, signal and idler photons. This study allows us to obtain quantum efficiency of the single photon detector by using photon pairs with various kinds of bandwidths.Comment: 21pages, 6figures, 4tables, accepted for publication in J. Opt. Soc. Am.

Inulin ameliorates metabolic syndrome in high-fat diet-fed mice by regulating gut microbiota and bile acid excretion

Background: Inulin is a natural plant extract that improves metabolic syndrome by modulating the gut microbiota. Changes in the gut microbiota may affect intestinal bile acids. We suggest that inulin may improve metabolism by inducing bile acid excretion by gut microbes.Methods: Male C57/BL mice were fed either a high-fat diet (60% calories) or a regular diet for 16 weeks, with oral inulin (10% w/w). At the end of the experiment, the gene expression levels (FGF15, CD36, Srebp-1c, FASN, and ACC) in the liver and intestines, as well as the serum levels of triglycerides (TGs), low-density lipoprotein (LDL) cholesterol, total cholesterol, and free fatty acids, were collected. The expression of FGF15 was examined using Western blot analysis. The fat distribution in the liver and groin was detected by oil red and hematoxylin and eosin staining. Simultaneously, the levels of serum inflammatory factors (alanine aminotransferase and aspartate aminotransferase) were detected to explore the side effects of inulin.Results: Inulin significantly improved glucose tolerance and insulin sensitivity, and decreased body weight and serum TG and LDL levels, in mice fed normal diet. Furthermore, inulin increased the α-diversity of the gut microbiota and increased the fecal bile acid and TG excretion in inulin-treated mice. In addition, inulin significantly reduced lipid accumulation in liver and inguinal fat, white fat weight, and hepatic steatosis. Western blot analysis showed that inulin reduced the expression of FGF15, a bile acid reabsorption protein.Conclusion: Inulin ameliorates the glucose and lipid metabolic phenotypes of mice fed a normal diet, including decreased intestinal lipid absorption, increased glucose tolerance, increased insulin sensitivity, and decreased body weight. These changes may be caused by an increase in bile acid excretion resulting from changes in the gut microbiota that affect intestinal lipid absorption

Biological control of the native endophytic fungus Pochonia chlamydosporia from the root nodule of Dolichos lablab on Fusarium wilt of banana TR4

Fusarium wilt of banana caused by Fusarium oxysporum f. sp. cubense, Tropical Race 4 (TR4) is a soil-borne disease, and it is devastating. At present, the biological control using antagonistic microorganisms to mitigate TR4 is one of the best strategies as a safe and green way. Yunnan has abundant and diverse microbial resources. Using the dual-culture method, the antagonistic endophytic fungi against TR4 were isolated and screened from the root nodule of Dolichos lablab. The effect of the highest antagonistic activity strain on the morphology of the TR4 mycelium was observed using the scanning electron microscope. According to morphological characteristics and sequence analysis, the strain was identified. The biocontrol effect and plant growth promotion were investigated by greenhouse pot experiment. Using the confocal laser scanning microscope and the real-time fluorescence quantitative PCR, the dynamics of TR4 infestation and the TR4 content in banana plant roots and corms would also be detected. In this study, 18 native endophytic fungi were isolated from a root nodule sample of Dolichos lablab in the mulch for banana fields in Yuxi, Yunnan Province, China. The YNF2217 strain showed a high antagonistic activity against TR4 in plate confrontation experiments, and the inhibition rate of YNF2217 is 77.63%. After TR4 culture with YNF2217 for 7 days in plate confrontation experiments, the morphology of the TR4 mycelium appeared deformed and swollen when observed under a scanning electron microscope. According to morphological characteristics and sequence analysis, the strain YNF2217 was identified as Pochonia chlamydosporia. In the greenhouse pot experiment, the biocontrol effect of YNF2217 fermentation solution on TR4 was 70.97% and 96.87% on banana plant leaves and corms, respectively. Furthermore, YNF2217 significantly promoted the growth of banana plants, such as plant height, leaf length, leaf width, leaf number, pseudostem girth, and both the aboveground and underground fresh weight. Observations of TR4 infestation dynamics in banana roots and corms, along with real-time fluorescence quantitative PCR, verified that YNF2217 inoculation could significantly reduce the TR4 content. Therefore, YNF2217 as P. chlamydosporia, which was found first time in China and reported here, is expected to be an important new fungal resource for the green control of Fusarium wilt of banana in the future

PathNarratives: Data annotation for pathological human-AI collaborative diagnosis

Pathology is the gold standard of clinical diagnosis. Artificial intelligence (AI) in pathology becomes a new trend, but it is still not widely used due to the lack of necessary explanations for pathologists to understand the rationale. Clinic-compliant explanations besides the diagnostic decision of pathological images are essential for AI model training to provide diagnostic suggestions assisting pathologists practice. In this study, we propose a new annotation form, PathNarratives, that includes a hierarchical decision-to-reason data structure, a narrative annotation process, and a multimodal interactive annotation tool. Following PathNarratives, we recruited 8 pathologist annotators to build a colorectal pathological dataset, CR-PathNarratives, containing 174 whole-slide images (WSIs). We further experiment on the dataset with classification and captioning tasks to explore the clinical scenarios of human-AI-collaborative pathological diagnosis. The classification tasks show that fine-grain prediction enhances the overall classification accuracy from 79.56 to 85.26%. In Human-AI collaboration experience, the trust and confidence scores from 8 pathologists raised from 3.88 to 4.63 with providing more details. Results show that the classification and captioning tasks achieve better results with reason labels, provide explainable clues for doctors to understand and make the final decision and thus can support a better experience of human-AI collaboration in pathological diagnosis. In the future, we plan to optimize the tools for the annotation process, and expand the datasets with more WSIs and covering more pathological domains

Patterns of Oncogene Coexpression at Single-Cell Resolution Influence Survival in Lymphoma

Cancers often overexpress multiple clinically relevant oncogenes, but it is not known if combinations of oncogenes in cellular subpopulations within a cancer influence clinical outcomes. Using quantitative multispectral imaging of the prognostically relevant oncogenes MYC, BCL2, and BCL6 in diffuse large B-cell lymphoma (DLBCL), we show that the percentage of cells with a unique combination MYC+BCL2+BCL6- (M+2+6-) consistently predicts survival across four independent cohorts (n = 449), an effect not observed with other combinations including M+2+6+. We show that the M+2+6- percentage can be mathematically derived from quantitative measurements of the individual oncogenes and correlates with survival in IHC (n = 316) and gene expression (n = 2,521) datasets. Comparative bulk/single-cell transcriptomic analyses of DLBCL samples and MYC/BCL2/BCL6-transformed primary B cells identify molecular features, including cyclin D2 and PI3K/AKT as candidate regulators of M+2+6- unfavorable biology. Similar analyses evaluating oncogenic combinations at single-cell resolution in other cancers may facilitate an understanding of cancer evolution and therapy resistance

A review of phase change heat transfer in shape-stabilized phase change materials (ss-PCMs) based on porous supports for thermal energy storage

Latent heat thermal energy storage (LHTES) uses phase change materials (PCMs) to store and release heat, and can effectively address the mismatch between energy supply and demand. However, it suffers from low thermal conductivity and the leakage problem. One of the solutions is integrating porous supports and PCMs to fabricate shape-stabilized phase change materials (ss-PCMs). The phase change heat transfer in porous ss-PCMs is of fundamental importance for determining thermal-fluidic behaviours and evaluating LHTES system performance. This paper reviews the recent experimental and numerical investigations on phase change heat transfer in porous ss-PCMs. Materials, methods, apparatuses and significant outcomes are included in the section of experimental studies and it is found that paraffin and metal foam are the most used PCM and porous support respectively in the current researches. Numerical advances are reviewed from the aspect of different simulation methods. Compared to representative elementary volume (REV)-scale simulation, the pore-scale simulation can provide extra flow and heat transfer characteristics in pores, exhibiting great potential for the simulation of mesoporous, microporous and hierarchical porous materials. Moreover, there exists a research gap between phase change heat transfer and material preparation. Finally, this review outlooks the future research topics of phase change heat transfer in porous ss-PCMs

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC