Ovarian tumor (OT) is the most lethal form of gynecologic malignancy, with minimal improvements in patient outcomes over the past several decades. Metastasis is the leading cause of ovarian cancer-related deaths, yet the underlying mechanisms remain poorly understood. Psychological stress is known to activate the glucocorticoid receptor (NR3C1), a factor associated with poor prognosis in OT patients. However, the precise mechanisms linking NR3C1 signaling and metastasis have yet to be fully elucidated. In this study, we demonstrate that chronic restraint stress accelerates epithelial-mesenchymal transition (EMT) and metastasis in OT through an NR3C1-dependent mechanism involving nuclear protein 1 (NUPR1). Mechanistically, NR3C1 directly regulates the transcription of NUPR1, which in turn increases the expression of snail family transcriptional repressor 2 (SNAI2), a key driver of EMT. Clinically, elevated NR3C1 positively correlates with NUPR1 expression in OT patients, and both are positively associated with poorer prognosis. Overall, our study identified the NR3C1/NUPR1 axis as a critical regulatory pathway in psychological stress-induced OT metastasis, suggesting a potential therapeutic target for intervention in OT metastasis.

To investigate the impact of preoperative serum follicle-stimulating hormone (FSH) levels on the probability of testicular sperm retrieval, we conducted a study of nonobstructive azoospermic (NOA) men with different testicular volumes (TVs) who underwent microdissection testicular sperm extraction (micro-TESE). A total of 177 NOA patients undergoing micro-TESE for the first time from April 2019 to November 2022 in Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital, Shenzhen, China) were retrospectively reviewed. The subjects were divided into four groups based on average TV quartiles. Serum hormone levels in each TV group were compared between positive and negative sperm retrieval subgroups. Overall sperm retrieval rate was 57.6%. FSH levels (median [interquartile range]) were higher in the positive sperm retrieval subgroup compared with the negative outcome subgroup when average TV was <5 ml (first quartile [Q1: TV <3 ml]: 43.32 [17.92] IU l -1 vs 32.95 [18.56] IU l -1 , P = 0.048; second quartile [Q2: 3 ml ≤ TV <5 ml]: 31.31 [15.37] IU l -1 vs 25.59 [18.40] IU l -1 , P = 0.042). Elevated serum FSH levels were associated with successful micro-TESE sperm retrieval in NOA men whose average TVs were <5 ml (adjusted odds ratio [OR]: 1.06 per unit increase; 95% confidence interval [CI]: 1.01-1.11; P = 0.011). In men with TVs ≥5 ml, larger TVs were associated with lower odds of sperm retrieval (adjusted OR: 0.84 per 1 ml increase; 95% CI: 0.71-0.98; P = 0.029). In conclusion, elevated serum FSH levels were associated with positive sperm retrieval in micro-TESE in NOA men with TVs <5 ml. In men with TV ≥5 ml, increases in average TVs were associated with lower odds of sperm retrieval.
Humans ; Male ; Azoospermia/surgery* ; Sperm Retrieval/statistics & numerical data* ; Adult ; Follicle Stimulating Hormone/blood* ; Retrospective Studies ; Testis/pathology* ; Microdissection ; Organ Size

OBJECTIVE: To investigate the relationship between serum levels of fibroblast growth factor-23 (FGF-23), heparanase (HPSE) and early renal impairment (RI) in patients with multiple myeloma (MM). METHODS: A retrospective analysis was conducted on the clinical data of 125 MM patients who were initially diagnosed in the Department of Hematology of the First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology from June 2020 to June 2023. The patients were divided into RI group (>176.80 μmol/L) and non-RI group (≤176.80 μmol/L) based on their serum creatinine levels when diagnosed. The baseline data and laboratory indexes of the two groups were compared. The relationship between serum FGF-23, HPSE and early RI in MM patients was analyzed. RESULTS: Among 125 newly diagnosed MM patients, 33 cases developed early RI, accounting for 26.40%. The proportion of light chain type, blood urea nitrogen (BUN), blood uric acid, lactate dehydrogenase, FGF-23, and HPSE levels in RI group were higher than those in non-RI group (all P <0.05). There was no statistical significant difference in other data between the two groups (P >0.05). Multivariate logistic regression analysis showed that BUN, FGF-23 and HPSE were associated with early RI in MM patients (all P <0.05). The serum FGF-23 level was divided into Q1-Q4 groups by quartile, and the serum HPSE level was divided into q1-q4 groups. The correlation analysis showed that with the increase of serum FGF-23 and HPSE levels, the incidence of early RI increased (r =0.668, 0.592). Furthermore, logistic regression analysis showed that after controlling for confounding factors, elevated levels of serum FGF-23 and HPSE were still influencing factors for early RI in MM patients (OR>1, P <0.05). According to Pearson's linear correlation test, there was a positive correlation between serum FGF-23 level and HPSE level (r =0.373). CONCLUSION There is a certain correlation between serum levels of FGF-23, HPSE and early RI in MM patients, and the incidence of early RI is higher in patients with abnormally high levels of both.
Humans ; Multiple Myeloma/complications* ; Fibroblast Growth Factor-23 ; Retrospective Studies ; Fibroblast Growth Factors/blood* ; Glucuronidase/blood* ; Male ; Female ; Middle Aged ; Renal Insufficiency/blood* ; Aged

Wastewater-based epidemiology has emerged as a transformative surveillance tool for estimating substance consumption and monitoring disease prevalence, particularly during the COVID-19 pandemic. It enables the population-level monitoring of illicit drug use, pathogen prevalence, and environmental pollutant exposure. In this perspective, we summarize the key challenges specific to the Chinese context: (1) Sampling inconsistencies, necessitating standardized 24-hour composite protocols with high-frequency autosamplers (≤ 15 min/event) to improve the representativeness of samples; (2) Biomarker validation, requiring rigorous assessment of excretion profiles and in-sewer stability; (3) Analytical method disparities, demanding inter-laboratory proficiency testing and the development of automated pretreatment instruments; (4) Catchment population dynamics, reducing estimation uncertainties through mobile phone data, flow-based models, or hydrochemical parameters; and (5) Ethical and data management concerns, including privacy risks for small communities, mitigated through data de-identification and tiered reporting platforms. To address these challenges, we propose an integrated framework that features adaptive sampling networks, multi-scale wastewater sample banks, biomarker databases with multidimensional metadata, and intelligent data dashboards. In summary, wastewater-based epidemiology offers unparalleled scalability for equitable health surveillance and can improve the health of the entire population by providing timely and objective information to guide the development of targeted policies.
China/epidemiology* ; Humans ; Wastewater/analysis* ; COVID-19/epidemiology* ; Public Health ; Wastewater-Based Epidemiological Monitoring ; SARS-CoV-2

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Objective To construct an aptamer-functionalized carboxylated graphene oxide(CGO)fluo-rescent sensor to achieve highly sensitive and specific detection of ketamine(KET)and its metabolite norketamine(NK)using an aptamer capable of simultaneously recognizing KET and NK.Methods A specific aptamer for simultaneous recognition of KET and NK was screened using graphene oxide-sys-tematic evolution of ligand by exponential enrichment(GO-SELEX)and molecular docking tech-niques.The aptamer,labeled with Cy5 fluorescence,was chemically conjugated to CGO to construct an aptamer-functionalized CGO fluorescent sensor.By optimizing detection conditions,including the mass concentration of CGO,aptamer concentration,reaction temperature,and incubation time,quantita-tive analysis of the target analytes was achieved using the ratio of fluorescence intensity changes be-fore and after target addition.The stability of the sensor in biological matrices was evaluated by moni-toring fluorescence intensity changes over incubation time in blank blood and urine,in comparison with the traditional physical adsorption-based CGO fluorescent sensor.Spiked recovery experiments in blank blood and urine were conducted to compare performance with that of HPLC-MS/MS.Results A specific aptamer A5 was selected and chemically conjugated with CGO to construct the aptamer-functionalized CGO fluorescent sensor.Under optimized conditions,the proposed fluorescent sensor ex-hibited a linear detection range of 1.0-5.0 ng/mL for KET,with a limit of detection(LOD)of 0.86 ng/mL;while for NK,the linear detection range was 1.0-5.0 ng/mL,with an LOD of 0.70 ng/mL.Com-pared with the CGO fluorescent sensor constructed via physical adsorption,this sensor demonstrated greater stability in blood and urine.The spiked recovery rates of KET and NK in blank blood and urine ranged from 81.50%to 110.03%,exhibiting detection performance comparable to that of HPLC-MS/MS.Conclusion The aptamer screening method offers a novel approach for selecting aptamers tar-geting drugs and their metabolites.The constructed aptamer-functionalized CGO fluorescent sensor pro-vides an efficient and reliable strategy for the high-performance detection of KET and NK.

Objective To investigate the correlation of serum human epididymis protein 4(HE4)and lung injury Prediction(LIPS)score with sepsis-associated acute respiratory distress syndrome(ARDS)and 28 d mortality.Methods A total of 207 patients with sepsis admitted to the hospital from January 2021 to January 2024 were selected.According to the presence or absence of ARDS,the patients were divided into ARDS group(73 cases)and non-ARDS group(134 cases).According to the 28 d survival,the ARDS group was further di-vided into death group(26 cases)and survival group(47 cases).HE4 level was detected by electrochemilumi-nescence,and LIPS score was calculated.Multivariate Logistic regression model was used to analyze the influ-encing factors of death in ARDS patients.Point biserial correlation was used to analyze the correlation between serum HE4,LIPS score and 28 d death in ARDS patients.The receiver operating characteristic(ROC)curve was used to evaluate the value of serum HE4 and LIPS score in the diagnosis of sepsis-associated ARDS and predicting 28 d mortality.Results Compared with non-ARDS group,the serum HE4 level and LIPS score were significantly increased in ARDS group(P<0.05).The independent risk factors for sepsis-associated ARDS were increased SOFA score,increased blood lactic acid,increased HE4 and increased LIPS score(P<0.05).The 28 d mortality of 73 patients with sepsis-associated ARDS was 35.62%(26/73).Compared with the survival group,the serum HE4 and LIPS scores were increased in the death group(P<0.05).Serum HE4 and LIPS scores were positively correlated with 28 d mortality in patients with sepsis-associated ARDS(r=0.605,0.579,P<0.05).The area under the curve(AUC)of serum HE4 combined with LIPS score in the di-agnosis of sepsis-associated ARDS was 0.909,which was larger than 0.828 and 0.806 of serum HE4 and LIPS score alone(Z=3.360,3.387,all P<0.05).The AUC of serum HE4 combined with LIPS score in predicting 28 d mortality of patients with sepsis-associated ARDS was 0.891,which was larger than 0.808 and 0.779 of by serum HE4 and LIPS score alone(Z=2.028,2.017,all P<0.05).Conclusion The elevated serum HE4 level and LIPS score are independent risk factors for sepsis-associated ARDS,and are closely related to 28 d mortality.The combination of serum HE4 and LIPS score has a high value in the diagnosis of sepsis-associated ARDS and the prediction of 28 d mortality.

This study employed bioinformatics to screen the feature genes related to efferocytosis in diabetic kidney disease(DKD) and explores traditional Chinese medicine(TCM) regulating these feature genes. The GSE96804 and GSE30528 datasets were integrated as the training set, and the intersection of differentially expressed genes and efferocytosis-related genes(ERGs) was identified as DKD-ERGs. Subsequently, correlation analysis, protein-protein interaction(PPI) network construction, enrichment analysis, and immune infiltration analysis were performed. Consensus clustering was conducted on DKD patients based on the expression levels of DKD-ERGs, and the expression levels, immune infiltration characteristics, and gene set variations between different subtypes were explored. Eight machine learning models were constructed and their prediction performance was evaluated. The best-performing model was evaluated by nomograms, calibration curves, and external datasets, followed by the identification of efferocytosis-related feature genes associated with DKD. Finally, potential TCMs that can regulate these feature genes were predicted. The results showed that the training set contained 640 differentially expressed genes, and after intersecting with ERGs, 12 DKD-ERGs were obtained, which demonstrated mutual regulation and immune modulation effects. Consensus clustering divided DKD into two subtypes, C1 and C2. The support vector machine(SVM) model had the best performance, predicting that growth arrest-specific protein 6(GAS6), S100 calcium-binding protein A9(S100A9), C-X3-C motif chemokine ligand 1(CX3CL1), 5'-nucleotidase(NT5E), and interleukin 33(IL33) were the feature genes of DKD. Potential TCMs with therapeutic effects included Astragali Radix, Trionycis Carapax, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma, which mainly function to clear heat, replenish deficiency, activate blood, resolve stasis, and promote urination and drain dampness. Molecular docking revealed that the key components of these TCMs, including β-sitosterol, quercetin, and sitosterol, exhibited good binding activity with the five target genes. These results indicated that efferocytosis played a crucial role in the development and progression of DKD. The feature genes closely related to both DKD and efferocytosis, such as GAS6, S100A9, CX3CL1, NT5E, and IL33, were identified. TCMs such as Astragali Radix, Trionycis Carapa, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma may provide a new therapeutic strategy for DKD by regulating efferocytosis.
Humans ; Computational Biology ; Diabetic Nephropathies/physiopathology* ; Protein Interaction Maps ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal ; Phagocytosis/genetics* ; Efferocytosis