Environmental damage affects many aspects of healthcare, from extreme weather events to evolving population disease. Singapore's healthcare sector has the world's second highest healthcare emissions per capita, hampering the nation's pledge to reduce emissions by 2030 and achieve net zero emissions by 2050. In this review, we provide an overview of the impact environmental damage has on healthcare, including facilities, supply chain and human health, and examine measures to address healthcare's impact on the environment. Utilising the 'R's of sustainability - rethinking, reducing/refusing, reusing/repurposing/reprocessing, repairing, recycling and research - we have summarised the opportunities and challenges across medical disciplines. Awareness and advocacy to adopt strategies at institutional and individual levels is needed to revolutionise our environmental footprint and improve healthcare sustainability. By leveraging evidence from ongoing trials and integrating sustainable practices, our healthcare system can remain resilient against environment-driven challenges and evolving healthcare demands while minimising further impacts of environmental destruction.
Humans ; Climate Change ; Delivery of Health Care ; Singapore ; Conservation of Natural Resources ; Sustainable Development ; Environment

The choice of biopsy method is critical in diagnosing prostate cancer (PCa). This retrospective cohort study compared systematic biopsy (SB) or cognitive fusion-targeted biopsy combined with SB (CB) in detecting PCa and clinically significant prostate cancer (csPCa). Data from 2572 men who underwent either SB or CB in Fudan University Shanghai Cancer Center (Shanghai, China) between January 2019 and December 2023 were analyzed. Propensity score matching (PSM) was used to balance baseline characteristics, and detection rates were compared before and after PSM. Subgroup analyses based on prostate-specific antigen (PSA) levels and Prostate Imaging-Reporting and Data System (PI-RADS) scores were performed. Primary and secondary outcomes were the detection rates of PCa and csPCa, respectively. Of 2572 men, 1778 were included in the PSM analysis. Before PSM, CB had higher detection rates for both PCa (62.9% vs 52.4%, odds ratio [OR]: 1.54, P < 0.001) and csPCa (54.9% vs 43.3%, OR: 1.60, P < 0.001) compared to SB. After PSM, CB remained superior in detecting PCa (63.1% vs 47.9%, OR: 1.86, P < 0.001) and csPCa (55.0% vs 38.2%, OR: 1.98, P < 0.001). In patients with PSA 4-12 ng ml -1 (>4 ng ml -1 and ≤12 ng ml -1 , which is also applicable to the following text), CB detected more PCa (59.8% vs 40.7%, OR: 2.17, P < 0.001) and csPCa (48.1% vs 27.7%, OR: 2.42, P < 0.001). CB also showed superior csPCa detection in those with PI-RADS 3 lesions (32.1% vs 18.0%, OR: 2.15, P = 0.038). Overall, CB significantly improves PCa and csPCa detection, especially in patients with PSA 4-12 ng ml -1 or PI-RADS 3 lesions.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Propensity Score ; Retrospective Studies ; Middle Aged ; Aged ; Image-Guided Biopsy/methods* ; Prostate-Specific Antigen/blood* ; Prostate/diagnostic imaging*

OBJECTIVE: To analyze the Epstein-Barr virus (EBV) load in different lymphocyte subsets, as well as clinical characteristics and outcomes in patients with hematologic malignancies experiencing EBV reactivation. METHODS: Peripheral blood samples from patients were collected. B, T, and NK cells were isolated sorting with magnetic beads by flow cytometry. The EBV load in each subset was quantitated by real-time quantitative polymerase chain reaction (RT-qPCR). Clinical data were colleted from electronic medical records. Survival status was followed up through outpatient visits and telephone calls. Statistical analyses were performed using SPSS 25.0. RESULTS: A total of 39 patients with hematologic malignancies were included, among whom 35 patients had undergone allogeneic hematopoietic stem cell transplantation (allo-HSCT). The median time to EBV reactivation was 4.8 months (range: 1.7-57.1 months) after allo-HSCT. EBV was detected in B, T, and NK cells in 20 patients, in B and T cells in 11 patients, and only in B cells in 4 patients. In the 35 patients, the median EBV load in B cells was 2.19×10⁴ copies/ml, significantly higher than that in T cells (4.00×10³ copies/ml, P <0.01) and NK cells (2.85×10² copies/ml, P <0.01). Rituximab (RTX) was administered for 32 patients, resulting in EBV negativity in 32 patients with a median time of 8 days (range: 2-39 days). Post-treatment analysis of 13 patients showed EBV were all negative in B, T, and NK cells. In the four non-transplant patients, the median time to EBV reactivation was 35 days (range: 1-328 days) after diagnosis of the primary disease. EBV was detected in one or two subsets of B, T, or NK cells, but not simultaneously in all three subsets. These patients received a combination chemotherapy targeting at the primary disease, with 3 patients achieving EBV negativity, and the median time to be negative was 40 days (range: 13-75 days). CONCLUSION In hematologic malignancy patients after allo-HSCT, EBV reactivation commonly involves B, T, and NK cells, with a significantly higher viral load in B cells compared to T and NK cells. Rituximab is effective for EBV clearance. In non-transplant patients, EBV reactivation is restricted to one or two lymphocyte subsets, and clearance is slower, highlighting the need for prompt anti-tumor therapy.
Humans ; Hematologic Neoplasms/virology* ; Herpesvirus 4, Human/physiology* ; Epstein-Barr Virus Infections ; Hematopoietic Stem Cell Transplantation ; Virus Activation ; Lymphocyte Subsets/virology* ; Flow Cytometry ; Killer Cells, Natural/virology* ; Male ; Female ; B-Lymphocytes/virology* ; Viral Load ; Adult ; T-Lymphocytes/virology* ; Middle Aged

AIM To establish a UPLC-ESI-MS/MS method for analyzing the toxic material basis of 95％ethanol cold soaked ultrasonic extract(EC),95％ethanol heated reflux extract(EH)and water decoction extract(WD)from Dryopteris crassirhizoma Nakai.METHODS The analysis was performed on a 25 ℃ thermostatic agilent ZORBAX RRHD StableBond C18 column(2.1 mm×150 mm,1.8 μm),with the mobile phase comprising of methanol-0.2％formic acid flowing at 0.30 mL/min,and heated electrospray ion source was adopted in positive and negative ion scanning.Compounds were identified by Compound Discover 3.3 software combined with the database and related literature,and the main differential components were screened by Heatmap cluster analysis and partial least squares discriminant analysis.RESULTS 72 compounds were identified(22 phloroglucinols,19 flavonoids,8 phenylpropanoids,6 terpenoids and 17 other components).The main toxic differential components were phloroglucinols such as flavaspidic acid AB,didemethylpseudoaspidin AA and filixic acid PBP,flavonoids such as(-)-epicatechin,(-)-epigallocatechin,cianidanol,and other compounds such as indole-3-carboxaldehyde.CONCLUSION This method can rapidly,effectively and comprehensively characterize the main chemical composition of D.crassirhizoma,and provide a reference for the study of its pharmacological mechanism.

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 investigate the predictive value of color Doppler ultrasound combined with electrocardio-gram for right heart dys function in patients with pulmonary heart disease(PHD).Methods:A total of 100 PHD patients admitted in Dongcheng Branch of First Affiliated Hospital of Anhui Medical University between January 2020 and December 2023 were retrospectively analyzed.According to results of 6min walking test(6MWT),pa-tients were divided into good right heart function group(n=64,≥350m)and right heart dysfunction group(n=36,＜350m).The indexes of cardiac color ultrasound[isovolumic relaxation time(IVRT),isovolumetric contraction time(IVCT)and right ventricular Tei index],ECG[24h mean R-R interval standard deviation(SDNN),normal R-R interval standard deviation per 5min(SDANN)and the ratio of low frequency components to high frequency components(LF/HF)]were compared between two groups.Receiver operating characteristic(ROC)curve was drawn to analyze the diagnostic value of color Doppler ultrasound,ECG and their combination for right heart dys-function in PHD patients.Spearman correlation coefficient was used to analyze the association of color Doppler ul-trasound,ECG and their combination with right heart dysfunction in PHD patients.Results:Compared with those in good right heart function group,patients in right heart dysfunction group had significant higher IVRT[(120.64±14.08)ms vs.(97.87±10.93)ms],IVCT[(84.28±12.33)ms vs.(71.92±10.61)ms]and Tei index[(0.85±0.11)vs.(0.63±0.07)](P＜0.001 all),and significant lower SDNN[(75.52±12.58)ms vs.(85.58±11.75)ms],SDANN[(63.86±10.92)ms vs.(76.75±11.71)ms]and LF/HF[(1.33±0.19)vs.(1.84±0.27)](P＜0.001 all).ROC curve indicated that the AUC of color Doppler ultrasound combined ECG in diagnosing right heart dysfunction in PHD patients was 0.911(95％CI 0.838～0.959),which was significantly higher than those of color Doppler ultrasound[0.775(95％CI 0.681～0.853),Z=2.404,P=0.016]and ECG[0.688(95％CI 0.588～0.777),Z=3.968,P=0.001]alone.Spearman correlation analysis indicated that there was a significant positive correlation of color Doppler ultrasound(r=0.547),ECG(r=0.375)and their combination(r=0.810)with right heart dysfunction in PHD patients(P＜0.001 all),and the correlation between combined detection and right heart dysfunction in PHD patients was significantly higher.Conclusion:Color Doppler ultrasound combined with ECG possesses high diagnostic performance for right heart dysfunction in PHD patients.

Objective:To construct a risk warning model for severe mycoplasma pneumoniae pneumonia(SMPP)children based on clinical data,laboratory indicators and imaging indicators.Methods:162 Mycoplasma pneumoniae pneumonia(MPP)children who were admitted in Foshan Women and Children Hospital from January 2021 to December 2023 were selected,64 SMPP children were included in severe group,the remaining 98 children were included in mild group.The general data,laboratory indicators and imaging indicators of the children were collected.The influencing factors for the occurrence of SMPP were analyzed by univariate and multivariate logistic regression models,and a risk warning model for the occurrence of SMPP children was constructed based on multivariate logistic regression model.The predictive value of the risk warning model for the occurrence of SMPP were analyzed by receiver operating characteristic(ROC)curve.Results:The proportion of 3 years old ≤ age＜6 years old,course of disease,body temperature,fever course,C-reactive protein(CRP),erythrocyte sedimentation rate(ESR),lactate dehydrogenase(LDH),cyanosis of lips,positive triconcave sign,pleural effusion,lesion site was the lower lobe,abnormal electrocardiogram and extrapulmonary manifestations in severe group were significantly higher than those in mild group(P＜0.05),there were no significant differences in gender,white blood cell count(WBC),neutrophil ratio and procalcitonin(PCT)between the two groups(P＞0.05).Multivariate logistic regression analysis model showed that,3 years old ≤age＜6 years old,high body temperature,long fever course,CRP elevated,ESR elevated,LDH elevated,cyanosis of lips,positive triconcave sign,pleural effusion,lesion site was the lower lobe,abnormal electrocardiogram and extrapulmonary manifestations were risk factors for the occurrence of SMPP(P＜0.05).ROC curve analysis showed that,the area under the curve(AUC)of the risk warning model was 0.829,the sensitivity was 84.82％,and the specificity was 78.15％,the actual prediction curve of the risk warning model was in good agreement with the prediction curve,the decision curve showed that,the threshold probability range of the model was 4.61％～88.14％.Conclusion:The risk warning model based on clinical multi parameters such as general data,laboratory indicators and imaging indicators has certain predictive value for the occurrence of SMPP.

Protein-protein interactions play an extremely important role in the biochemical functions of cells,and in-depth analysis of protein interactions is the key to understanding cellular life activities.In this study,we systematically mined the interacting proteins of Golgi protein 73(GP73)using classical immunoprecipitation combined with mass spectrometry,and sought to further analyze the molecular func-tion of GP73.Hepatocellular carcinoma cell line HepG2 was selected,and a stable cell line overexpress-ing GP73-3Flag was constructed using lentiviral infection technology.A total of 78 high-confidence GP73 interacting proteins were identified by immunoprecipitation coupled with mass spectrometry.Bioinformat-ics analyses suggested that GP73 interacted with nearly 40 cytosolic proteins and participated in the bio-logical processes of RNA transport,splicing,and translation.Further immunofluorescence and cytosolic protein isolation experiments confirmed the cytosolic localization of GP73 in a variety of tumor cells.Based on the 78 interacting proteins,we further screened protein interaction networks related to mRNA splicing and verified the existence of interactions between GP73 and seven proteins,including HNRN-PH3,SMN1,RBM14,andNCBP1,by co-immunoprecipitation experiments.In addition,minigene spli-cing assay results indicated that GP73 inhibited the splicing efficiency of pre-mRNA by cells.This study contributes to the expansion of knowledge regarding the function of GP73 and aids in elucidating its criti-cal role in cell biology and its potential association with diseases.

Objective:To construct a risk warning model for severe mycoplasma pneumoniae pneumonia(SMPP)children based on clinical data,laboratory indicators and imaging indicators.Methods:162 Mycoplasma pneumoniae pneumonia(MPP)children who were admitted in Foshan Women and Children Hospital from January 2021 to December 2023 were selected,64 SMPP children were included in severe group,the remaining 98 children were included in mild group.The general data,laboratory indicators and imaging indicators of the children were collected.The influencing factors for the occurrence of SMPP were analyzed by univariate and multivariate logistic regression models,and a risk warning model for the occurrence of SMPP children was constructed based on multivariate logistic regression model.The predictive value of the risk warning model for the occurrence of SMPP were analyzed by receiver operating characteristic(ROC)curve.Results:The proportion of 3 years old ≤ age＜6 years old,course of disease,body temperature,fever course,C-reactive protein(CRP),erythrocyte sedimentation rate(ESR),lactate dehydrogenase(LDH),cyanosis of lips,positive triconcave sign,pleural effusion,lesion site was the lower lobe,abnormal electrocardiogram and extrapulmonary manifestations in severe group were significantly higher than those in mild group(P＜0.05),there were no significant differences in gender,white blood cell count(WBC),neutrophil ratio and procalcitonin(PCT)between the two groups(P＞0.05).Multivariate logistic regression analysis model showed that,3 years old ≤age＜6 years old,high body temperature,long fever course,CRP elevated,ESR elevated,LDH elevated,cyanosis of lips,positive triconcave sign,pleural effusion,lesion site was the lower lobe,abnormal electrocardiogram and extrapulmonary manifestations were risk factors for the occurrence of SMPP(P＜0.05).ROC curve analysis showed that,the area under the curve(AUC)of the risk warning model was 0.829,the sensitivity was 84.82％,and the specificity was 78.15％,the actual prediction curve of the risk warning model was in good agreement with the prediction curve,the decision curve showed that,the threshold probability range of the model was 4.61％～88.14％.Conclusion:The risk warning model based on clinical multi parameters such as general data,laboratory indicators and imaging indicators has certain predictive value for the occurrence of SMPP.