The recombinant human thyroid-stimulating hormone (rhTSH) can be used for differentiated thyroid cancer (DTC) preparation before ¹³¹I therapy, and follow-up. Since its approval by the American Food and Drug Administration (FDA) in 1998, rhTSH has been clinically used in European countries, the United States, and other countries for nearly 30 years. In April 2024, China’s first independently developed rhTSH agent was approved. Based on clinical experience and evidence from domestic and international studies, an expert consensus on the application of rhTSH has been developed following thorough consultations among experts. The consensus offers evidence-based recommendations to guide the appropriate use of rhTSH in clinical practice.

Objective To analyze the differences of free and esterified oxo-eicosatetraenoic acids (oxo-ETEs) in blood cells and plasma from arterial and venous blood in hemodialysis (HD) patients. Methods Arterial and venous blood samples from 12 patients with end-stage renal disease (ESRD) before and after HD treatment at Charité – Universitätsmedizin Berlin, Germany, from June to December 2020 were collected. The esterified and free oxo-ETEs derived from arachidonic acid in blood cells and plasma were measured by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Results Neither esterified nor free oxo-ETEs in blood cells displayed significant arteriovenous differences before and after HD. HD predominantly affected the metabolic levels of esterified and free oxo-ETEs in plasma. HD reduced the arteriovenous differences of esterified 12-oxo-ETE, free 15-oxo-ETE, and free 5-oxo-ETE in plasma, while raised the arteriovenous differences of esterified 15-oxo-ETE. Conclusions The oxo-ETEs in blood cells are relatively well-stabilized responding to HD treatment, whereas arteriovenous differences of free and esterified oxo-ETEs in plasma are present and active in response to HD treatment, potentially contributing to the cardiovascular disease.

Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans ; Male ; Azoospermia/genetics* ; Meiosis/genetics* ; Spermatogenesis/genetics* ; Adult ; Exome Sequencing ; Microtubule-Associated Proteins/genetics* ; Alleles ; Infertility, Male/genetics*

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

The chemical composition of Paeoniae Radix Alba(PRA) is complex, with primary secondary metabolites including monoterpenoids, tannins, triterpenoids, and flavonoids. In previous studies on the material basis of PRA, it was found that, in addition to the widely studied characteristic monoterpene glycosides, tannic acid components also play an important role in the efficacy of PRA. However, their pharmacological effects have not been thoroughly investigated. This paper reviews the tannic acid components in PRA, including pentagaloyl glucose(PGG), tetragaloyl glucose(TGG), trigaloyl glucose(TriGG), and gallic acid, along with their structures, properties, and characteristics to provide a detailed discussion of their pharmacological activities and related mechanisms, aiming to offer a theoretical basis for the material basis research and clinical application of PRA.
Paeonia/chemistry* ; Tannins/chemistry* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Animals ; Plant Extracts

Objective: To investigate the role of butyric acid-producing bacteria in long bone homeostasis in mice with periodontitis under a high-fat/high-sugar diet and to provide new insights for the prevention and treatment of periodontitis and related bone metabolic diseases. Methods: This study has been approved by the Animal Welfare and Ethics Committee of the Experimental Animal Center. Initially, 14 mice were randomly divided into the CON group (the control group) and the LIG group (the periodontitis group). Mice in the LIG group had experimental periodontitis induced by ligating the second maxillary molars bilaterally and were fed a high-fat and high-sugar diet. After 8 weeks, samples were collected. Micro-computed tomography (Micro-CT) was used to analyze alveolar bone resorption and various parameters of the proximal tibia trabecular bone, including bone mineral density (BMD), bone volume per tissue volume (BV/TV), trabecular number (Tb.N), trabecular thickness (Tb.Th), and trabecular separation (Tb.Sp). After decalcification, hematoxylin and eosin (HE) staining was performed on maxillary bone sections to assess periodontal tissue inflammation and connective tissue destruction. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect related genes in the distal femur and proximal tibia bone tissues, including osteocalcin (OCN), osteogenic transcription factor (Osterix), osteoprotegerin (OPG), tartrate resistant acid phosphatase (TRAP), osteoclast-associated receptor (OSCAR), receptor activator of nuclear factor kappa-B (RANK), and receptor activator of nuclear factor kappa-B ligand (RANK-L). Subsequently, the other 28 mice were randomly divided into the CON group (the control group), LIG group (the periodontitis group), CON + butyric acid-producing bacteria (BP) group, and LIG + BP group. The breeding, sampling, and sample detection methods remained the same. Finally, the other 28 mice were randomly divided into the CON group (the control group), LIG group (the periodontitis group), CON + sodium butyrate (SB) group, and LIG + SB group. The breeding, sampling, and sample detection methods remained the same. Results: ①Periodontitis modeling was successful. Compared with the CON group, the LIG group exhibited significant alveolar bone resorption of the maxillary second molar, aggravated periodontal tissue inflammation, and connective tissue destruction. ②Periodontitis exacerbated long bone resorption in mice fed a high-fat high-sugar diet. Compared with the CON group, the LIG group had significantly lower BMD, BV/TV, Tb.N, and Tb.Th (P<0.05), and significantly higher Tb.Sp (P<0.05). HE staining of the proximal tibia showed that the trabeculae in the LIG group were sparse and disordered, with some areas showing fractures or dissolution. The expression of osteoblast markers (OCN, Osterix, OPG) was significantly lower in the LIG group (P<0.05), while the expression of the osteoclast marker TRAP showed an increasing trend (P>0.05). The ratio of RANK-L/OPG was significantly higher in the LIG group compared with the CON group (P<0.05). ③ Supplementation with butyric acid-producing bacteria alleviates periodontitis-induced disruption of long bone homeostasis in mice fed a high-fat/high-sugar diet. Compared with the LIG group, BMD and Tb.Th were significantly higher in the LIG + BP group. HE staining of the proximal tibia showed that bone resorption was mitigated in the LIG + BP group compared with the LIG group. The expression of OCN and Osterix was significantly higher in the LIG + BP group, while the expression of osteoclast-specific genes (OSCAR, RANK, RANK-L) was significantly lower (P<0.05). ④ Supplementation with butyrate alleviates periodontitis-induced disruption of long bone homeostasis in mice fed a high-fat/high-sugar diet. Compared with the LIG group, BV/TV and Tb.N were significantly higher in the LIG + SB group, and Tb.Sp was significantly lower (P<0.05). HE staining of the proximal tibia showed that bone resorption was mitigated in the LIG + SB group compared with the LIG group. The expression of Osterix, OPG, OSCAR, TRAP, and RANK was significantly lower in the LIG + SB group compared with the LIG group (P<0.05). Conclusion Periodontitis disrupts the long bone homeostasis of mice fed a high-fat high-sugar diet, aggravating long bone resorption. Supplementation with butyric acid-producing bacteria or butyrate can effectively alleviate the disruption of long bone homeostasis caused by periodontitis.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.