With Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum drug pair as the research object, supramolecular chemistry of traditional Chinese medicine(TCM) was used to study differences between the compatibility of herbal medicine Glycyrrhizae Radix et Rhizoma with mineral medicine Gypsum Fibrosum and its main component CaSO_4·2H_2O, so as to preliminarily discuss the scientific connotation of compatibility of Gypsum Fibrosum in clinical application. A Malvern particle sizer, a scanning electron microscope(SEM), and a conductivity meter were used to observe and determine the physical properties such as microscopic morphology, particle size, and conductivity of Gypsum Fibrosum, CaSO_4·2H_2O, and water decoctions of them with Glycyrrhizae Radix et Rhizoma. An inductively coupled plasma optical emission spectrometer(ICP-OES) was employed to detect the inorganic metal elements in Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum and Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O. Isothermal titration calorimetry(ITC) was conducted to quantify the interactions of Gypsum Fibrosum and CaSO_4·2H_2O with Glycyrrhizae Radix et Rhizoma. A Fourier transform infrared spectrometer(FTIR) was used to analyze the characteristic absorption peak change of Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum and Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O. X-ray diffraction(XRD) was performed to determine the crystal structure and phase composition of Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum and Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O. Further, glycyrrhizic acid(GA) was substituted for Glycyrrhizae Radix et Rhizoma to co-decoct with Gypsum Fibrosum, CaSO_4·2H_2O, and freeze-dried powder of their respective water decoctions. The results of XRD were used for verification analysis. The results showed that although CaSO_4·2H_2O is the main component of Gypsum Fibrosum, there were significant differences between their decoctions and between the decoctions of them with Glycyrrhizae Radix et Rhizoma. Specifically,(1) Both CaSO_4·2H_2O and Gypsum Fibrosum were amorphous fibrous. However, the particle size and conductivity were significantly different between the decoctions of CaSO_4·2H_2O and Gypsum Fibrosum alone.(2) Under SEM, Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O was a hybrid system with various morphologies, while Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum presented uniform nanoparticles.(3) The particle sizes and conductivities of Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O and Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum were significantly different and did not follow the same tendency as those of the decoctions of CaSO_4·2H_2O and Gypsum Fibrosum alone.(4) Compared with Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O, Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum had stronger molecular binding ability and functional group structure change.(5) The crystal form was largely different between the freeze-dried powder of CaSO_4·2H_2O decoction and Gypsum Fibrosum decoction, and their crystal forms were also significantly different from those of the freeze-dried powder of Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O and Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum decoctions. The reason for the series of differences is that Gypsum Fibrosum is richer in trace elements than CaSO_4·2H_2O. The XRD results of GA-Gypsum Fibrosum and GA-CaSO_4·2H_2O decoctions further prove the importance of trace elements in Gypsum Fibrosum for supramolecule formation. This research preliminarily reveals the influence of compatibility of Gypsum Fibrosum or CaSO_4·2H_2O on decoction phase state, material form, and crystal form, providing a basis for the rational clinical application of Gypsum Fibrosum.
Drugs, Chinese Herbal/chemistry* ; Calcium Sulfate/chemistry* ; Glycyrrhiza/chemistry* ; Crystallization ; Particle Size ; Medicine, Chinese Traditional ; Rhizome/chemistry*

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*

Anti-tumor traditional Chinese medicine has a long history of clinic application, in which the star molecules have always been the hotspot of modern drug research, but they are limited by the solubility, stability, targeting, bioactivity or toxicity of the monomer components of traditional Chinese medicine anti-tumor star molecules and other pharmacokinetic problems, which hinders the traditional Chinese medicine anti-tumor star molecules for further clinical translation and application. Currently, the nanosystems prepared by supramolecular technologies such as molecular self-assembly and nanomaterial encapsulation have broader application prospects in improving the anti-tumor effect of active components of traditional Chinese medicine, which has attracted extensive attention from scholars at home and abroad. In this paper, we systematically review the research progress in preparation of supramolecular nano-systems from anti-tumor star molecule of traditional Chinese medicine, and summarize the two major categories and ten small classes of carrier-free and carrier-based supramolecular nanosystems and their research cases, and the future development direction is put forward. The purpose of this paper is to provide reference for the research and clinical transformation of using supramolecular technology to improve the clinical application of anti-tumor star molecule of traditional Chinese medicine.

Phosphatase and tensin-homolog deleted on chromosome 10 (PTEN) is an important cancer suppressor gene. Its pathogenic mutation leads to PTEN hamartoma tumor syndrome (PHTS), a rare syndrome also known as Cowden syndrome, which is relevant to early-onset hereditary breast cancer (BC). In this paper, we report three patients with unilateral multicentric BC and synchronous and metachronous bilateral BC who harbored PTEN gene mutations, and summarize the clinical manifestations, pathological characteristics, diagnosis, treatment and follow-up outcomes to provide reference for management of PTEN gene mutation-related BC among the Cowden syndrome population.

Objective This study aims at establishing a teaching catalog and content for breast rare dis-eases and developing the syllabus for the breast rare disease using integrated multimodality of case-/problem-/resource-based learning(CBL+PBL+RBL).Methods By conducting bibliometrics co-occurrence analysis,we collected 6291 articles on breast rare disease published from January,1975 to June,2024.Additionally,we re-trieved the Textbook on Rare Diseases,the Catalog of Chinese Rare Disease,and Second Batch of Rare Dis-ease Catalog and then decided the teaching content.Results From 16,387 keywords,1000(6.1％)keywords were identified through co-occurrence analysis,including 50(0.3％)candidate diseases.These were classified into three categories:rare primary breast diseases,rare genetic mutation-related diseases associated with breast cancer,and rare systemic multi-system diseases involving the breast.From the candidate list,20(0.1％)rare primary breast diseases were further selected for their notable clinical teaching significance,and significant multi-systemic diseases affecting the breast,whether related to gene mutations or not.Teaching plans were draf-ted using a diversified parallel teaching approaches,taking into account the characteristics of different diseases and the focus of different teaching methods.Conclusions This study initiated the development of the teaching content for breast rare diseases and developed the teaching syllabus using the CBL+PBL+RBL integrated multi teaching model and targeting each rare breast disease for the critical point for teaching.

It remains unclear whether heart transplantation is still feasible on patients who develop neurological complications before surgery and underwent successful neurological treatment.This article reported the diagnosis and treatment process of a heart failure patient complicating with acute ischemic stroke,who underwent successful heart transplantation after thrombectomy for acute ischemic stroke,aiming to provide clinical evidence and decision-making plan on diagnosis and treatment options for this group of patients with severe neurological complications.

Objective To explore the effect and mechanism of hydroxysafflor yellow A(HSYA)on autophagy in bEnd.3 cells after oxygen-glucose deprivation(OGD).Methods The bEnd.3 cells were divided into normal group(conventional culture),model group(OGD model),HSYA group(OGD model+75 μmol·L-1 HSYA),3-methyladenine(3MA)group(5 mmol·L-1 3MA+OGD model)and 3 MA+HSYA group(5 mmol·L-1 3 MA+OGD model+75 μmol·L-1 HSYA).The level of apoptosis was determined by TUNEL fluorescence staining;Western blot was used to detect the expression of autophagy,blood brain barrier(BBB)related proteins;real time fluorescence quantitative polymerase chain reaction method for determining the expression of sirtuin-1(SIRT1)and forkhead box protein O3a(FOXO3A)mRNA.Results In the normal group,model group,HSYA group,3MA group and 3MA+HSYA group,the positive cells selected for TUNEL staining were 5.00±1.00,28.00±2.00,21.00±3.00,35.33±2.51 and 29.67±2.52;the expression levels of microtubule-associated protein 1 light chain 3-Ⅱ/-Ⅰ(LC3-Ⅱ/-Ⅰ)were 0.90±0.20,1.34±0.10,1.95±0.14,0.76±0.15 and 1.14±0.09;sequestosome 1(P62)were 0.99±0.02,0.60±0.02,0.38±0.01,0.67±0.04 and 0.54±0.01;occludin were 1.39±0.17,0.62±0.15,1.00±0.09,0.40±0.13 and 0.80±0.15;zonula occludens-1(ZO-1)were 1.63±0.20,0.64±0.06,0.98±0.14,0.37±0.14 and 0.87±0.04;SIRT1 mRNA were 1.00±0.00,0.75±0.07,1.69±0.09,0.31±0.02 and 0.56±0.01;FOXO3A mRNA were 1.00±0.00,0.80±0.05,1.47±0.09,0.40±0.01 and 0.62±0.09,respectively.Significant differences were found between model group and normal group,HSYA group and model group,3MA+HSYA group and 3MA group(P＜0.05,P＜0.01,P＜0.001).Conclusion HSYA may enhance autophagy levels in bEnd.3 cells after OGD through the SIRT1/FOXO3A pathway,inhibit cell apoptosis and alleviate BBB damage.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.