Under the background of the integrated development of the Guangdong-Hong Kong-Macao Greater Bay Area,the demand for medical services from Hong Kong and Macao residents in Guangdong continues to grow.As the main force in pro-viding medical services in Guangdong,public hospitals play a crucial role.This article aims to explore the current situation of the development of medical services for Hong Kong and Macao residents in Guangdong and propose development strategies from the perspective of hospitals to contribute to the integrated development of the Greater Bay Area.The SWOT method is used to system-atically evaluate the strengths,weaknesses,opportunities,and threats of Guangdong public hospitals in the development of medi-cal services for Hong Kong and Macao residents.A SWOT matrix is then constructed to formulate development countermeasures.The analysis shows that Guangdong public hospitals have advantages such as convenient transportation,cultural affinity,abundant medical resources,high service efficiency,and relatively low costs.However,they also face challenges such as inadequate cover-age of cross-border medical insurance,immature specialized services,inconsistent service standards,and insufficient exploration of cross-border medical care.At the same time,there are opportunities provided by national strategic support and strong market demand,while fierce market competition and slow integration of medical regulations among the three regions pose external threats.Based on the analysis,this article proposes countermeasures and suggestions,including strengthening publicity and innovative in-dustry cooperation,phased development of business,striving for policy coverage,enhancing business coordination,accelerating cooperation with the Hong Kong and Macao in medical care,and promoting international accreditation of hospitals.These sugges-tions provide decision-making references for the development of medical services for Hong Kong and Macao residents in Guang-dong's public hospitals.

(rsFC)and their relationship with negative symptoms in schizophrenia patients with predominant negative symptoms(PNS).Methods Fifty-four schizophrenia patients with PNS and sixty-one healthy controls underwent resting-state functional magnetic resonance imaging(fMRI)scans.Data were collected on general demographic information,the Positive and Negative Syndrome Scale(PANSS),the Scale for the Assessment of Negative Symptoms(SANS),and the Temporal Experience of Pleasure Scale(TEPS).Twelve striatal subregions were selected as regions of interest(ROIs)to analyze the rsFC between each ROI and whole-brain voxels.The rsFC values of areas with significant differences were extracted for Pearson correlation analysis with negative symptoms.Results Compared with healthy controls,schizophrenia patients with PNS exhibited decreased rsFC between the right dorsal caudal putamen(DCP)and right insula,left middle frontal gyrus(MFG),right median cingulate and paracingulate gyri(MCC);between the left DCP and right putamen,left insula,left MFG;between the right dorsal rostral putamen(DRP)and bilateral MFG,left insula,right MCC;between the left DRP and right insula,left rolandic operculum;between the right ventral rostral putamen(VRP)and bilateral putamen,left MFG,right MCC;between the left VRP and right insula,left putamen,bilateral MFG,right MCC,left inferior parietal gyrus,excluding supramarginal and angular gyri.Decreased rsFC was also observed between the left ventral caudate/nucleus accumbens(inferior)and right insula,left anterior cingulate cortex,supracallosal,bilateral precuneus(a threshold of P<0.001 in voxel-level with P<0.05 in cluster-lever,corrected for family-wise error,PFWE<0.05/12=0.004).No regions showed increased rsFC in schizophrenia patients with PNS relative to healthy controls.And no significant correlations were found between striatal rsFC and negative symptoms(PBonferroni>0.05).Conclusion Schizophrenia patients with PNS exhibited widespread cortical-striatal functional connectivity abnormalities,particularly reduced rsFC between the putamen and the MFG,MCC and insula.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

BACKGROUND: Previous evidence showed that ambient air pollution and cardiovascular mortality are related. However, there is a lack of evidence towards the modification effect of long-term lifestyle on the association between short-term ambient air pollution and death from cardiovascular events. METHOD: A total of 14,609 death from major adverse cardiovascular events (MACE) were identified among the China Kadoorie Biobank participants from 2013 to 2018. Ambient air pollution exposure including particulate matter 2.5 (PM_2.5), SO₂, NO₂, CO, and O₃ from the same period were obtained from space-time model reconstructions based on remote sensing data. Case-crossover design and conditional logistic regression was applied to estimate the effect of short-term exposure to air pollutants on MACE mortality. RESULTS: We found MACE mortality was significantly associated with PM_2.5 (relative percent increase 2.91% per 10 µg/m³ increase, 95% CI 1.32-4.53), NO₂ (5.37% per 10 µg/m³ increase, 95% CI 1.56-9.33), SO₂ (6.82% per 10 µg/m³ increase, 95% CI 2.99-10.80), and CO (2.24% per 0.1 mg/m³ increase, 95% CI 1.02-3.48). Stratified analyses indicated that drinking was associated with elevated risk of MACE mortality with NO₂ and SO₂ exposure; physical inactivity was associated with higher risk of death from MACE when exposed to PM_2.5; and people who had balanced diet had lower risk of MACE mortality when exposed to CO and NO₂. CONCLUSIONS The study results showed that short-term exposure to ambient PM_2.5, NO₂, SO₂, and CO would aggravate the risk of cardiovascular mortality, yet healthy lifestyle conduct might mitigate such negative impact to some extent.
Humans ; Cardiovascular Diseases/epidemiology* ; China/epidemiology* ; Male ; Female ; Air Pollution/adverse effects* ; Middle Aged ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Environmental Exposure/adverse effects* ; Life Style ; Aged ; Adult ; Risk Factors ; Cross-Over Studies ; East Asian People

(rsFC)and their relationship with negative symptoms in schizophrenia patients with predominant negative symptoms(PNS).Methods Fifty-four schizophrenia patients with PNS and sixty-one healthy controls underwent resting-state functional magnetic resonance imaging(fMRI)scans.Data were collected on general demographic information,the Positive and Negative Syndrome Scale(PANSS),the Scale for the Assessment of Negative Symptoms(SANS),and the Temporal Experience of Pleasure Scale(TEPS).Twelve striatal subregions were selected as regions of interest(ROIs)to analyze the rsFC between each ROI and whole-brain voxels.The rsFC values of areas with significant differences were extracted for Pearson correlation analysis with negative symptoms.Results Compared with healthy controls,schizophrenia patients with PNS exhibited decreased rsFC between the right dorsal caudal putamen(DCP)and right insula,left middle frontal gyrus(MFG),right median cingulate and paracingulate gyri(MCC);between the left DCP and right putamen,left insula,left MFG;between the right dorsal rostral putamen(DRP)and bilateral MFG,left insula,right MCC;between the left DRP and right insula,left rolandic operculum;between the right ventral rostral putamen(VRP)and bilateral putamen,left MFG,right MCC;between the left VRP and right insula,left putamen,bilateral MFG,right MCC,left inferior parietal gyrus,excluding supramarginal and angular gyri.Decreased rsFC was also observed between the left ventral caudate/nucleus accumbens(inferior)and right insula,left anterior cingulate cortex,supracallosal,bilateral precuneus(a threshold of P<0.001 in voxel-level with P<0.05 in cluster-lever,corrected for family-wise error,PFWE<0.05/12=0.004).No regions showed increased rsFC in schizophrenia patients with PNS relative to healthy controls.And no significant correlations were found between striatal rsFC and negative symptoms(PBonferroni>0.05).Conclusion Schizophrenia patients with PNS exhibited widespread cortical-striatal functional connectivity abnormalities,particularly reduced rsFC between the putamen and the MFG,MCC and insula.

Under the background of the integrated development of the Guangdong-Hong Kong-Macao Greater Bay Area,the demand for medical services from Hong Kong and Macao residents in Guangdong continues to grow.As the main force in pro-viding medical services in Guangdong,public hospitals play a crucial role.This article aims to explore the current situation of the development of medical services for Hong Kong and Macao residents in Guangdong and propose development strategies from the perspective of hospitals to contribute to the integrated development of the Greater Bay Area.The SWOT method is used to system-atically evaluate the strengths,weaknesses,opportunities,and threats of Guangdong public hospitals in the development of medi-cal services for Hong Kong and Macao residents.A SWOT matrix is then constructed to formulate development countermeasures.The analysis shows that Guangdong public hospitals have advantages such as convenient transportation,cultural affinity,abundant medical resources,high service efficiency,and relatively low costs.However,they also face challenges such as inadequate cover-age of cross-border medical insurance,immature specialized services,inconsistent service standards,and insufficient exploration of cross-border medical care.At the same time,there are opportunities provided by national strategic support and strong market demand,while fierce market competition and slow integration of medical regulations among the three regions pose external threats.Based on the analysis,this article proposes countermeasures and suggestions,including strengthening publicity and innovative in-dustry cooperation,phased development of business,striving for policy coverage,enhancing business coordination,accelerating cooperation with the Hong Kong and Macao in medical care,and promoting international accreditation of hospitals.These sugges-tions provide decision-making references for the development of medical services for Hong Kong and Macao residents in Guang-dong's public hospitals.