AIM: To investigate variation patterns of corneal biomechanical parameters and binocular symmetry among children of different genders aged 8-16 years.METHODS:A retrospective study was conducted, and children who underwent optometric examinations at the ophthalmology department of our hospital were enrolled between January 2022 and December 2024. Measurements included the flat keratometry(K1), steep keratometry(K2), and mean curvature(Km)of the anterior corneal surface, horizontal visible iris diameter(HVID), central corneal thickness(CCT), corneal endothelial cell density(CECD), average cell size(ACS), coefficient of variation(CV), and hexagonality(HEX). Corneal parameters and binocular differences were compared between genders and across age groups.RESULTS:A total of 621 children(1 242 eyes)were enrolled in this study, including 284 males(568 eyes), 337 females(674 eyes), 528 children aged 8-12 years(1 056 eyes), and 93 children aged 13-16 years(186 eyes). In children aged 8-16 years, the K1, K2, Km and CV of both eyes, as well as the interocular CCT differences in boys were significantly lower than those in girls(all P<0.05), while the HVID and HEX of both eyes, as well as the CCT of the left eye in boys were significantly higher than those in girls(all P<0.05). Children aged 8-12 years had significantly higher K1, Km, CECD and HEX in both eyes, and significantly lower ACS in both eyes than those aged 13-16 years(all P<0.05). K1, K2, Km, CECD and HEX in both eyes were negatively correlated with age(P<0.05); ACS in both eyes was positively correlated with age(P<0.001); K1 and Km of the right eye were positively correlated with the CECD of the right eye(P<0.05), and K1 and CCT of the left eye were positively correlated with the CECD of the left eye(P<0.05).CONCLUSION:Significant gender differences exist in corneal parameters among children aged 8 to 16 years, while binocular symmetry remained stable.

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.

In this study, RAW264.7 cells were employed to investigate the effects of honey-processed Astragalus on their energy metabolism and polarization, and explore the scientific connotation of the enhanced efficacy of honey-processed Astragalus on invigorating spleen-stomach and replenishing Qi. The medicated sera were prepared by intragastric administration of rats with water extracts of crude and honey-processed Astragalus, and the composition changes of medicated sera of crude and honey-processed Astragalus were analyzed by using LC-MS technology. The cell survival rates were detected and the concentrations of medicated sera were screened through CCK-8 assay. The differences of cell phagocytic rates, ATP energy metabolism, and NO secretion between crude and honey-processed Astragalus were evaluated by using neutral red phagocytosis assay, ATP detection kit, and NO detection kit. The effects of crude and honey-processed Astragalus on TNF-α secretion of RAW264.7 cells were detected by employing ELISA kit. The effects of crude and honey-processed Astragalus on polarization of RAW264.7 cells were evaluated by utilizing flow cytometry. The differential metabolites related to glycolysis in cell lysates and culture media were screened by using LC-MS technology. The experiment was approved by the experimental animal ethics committee from Shanxi University of Chinese Medicine (No. AWE202407352). The results showed that the contents of betaine, amino acids, and ononin in the prepared medicated sera of rats treated by intragastric administration with water extracts of honey-processed Astragalus increased compared to those in crude one. The results of CCK-8 experiment showed that there were no cytotoxic effects on RAW264.7 cells in the medicated sera of crude and honey-processd Astragalus at different concentration. The phagocytic index and ATP yield both increased to varying degrees after administration of the medicated sera to cells. The secretion of NO in normal and inflammatory cells increased and decreased respectively, and the effect of honey-processed Astragalus was better than that of crude one. The results of ELISA kit showed that the medicated sera of both crude and honey-processed Astragalus could promote the secretion of TNF-α in a concentration-dependent manner, and the promoting effect of honey-processed Astragalus was stronger than that of crude one. The results of flow cytometry showed that the medicated sera of both crude and honey-processed Astragalus could promote M1-type polarization and inhibit M2-type polarization of RAW264.7 cells, and the effect of honey-processed Astragalus was better than that of crude one. Compared to crude Astragalus, the metabolites related to glycolysis in the cell lysates and culture media of the medicated sera of honey-processed Astragalus were generally on the rise, indicating that the effect on promoting glycolysis of honey-processed Astragalus was better than that of crude one. In summary, honey-processed Astragalus can promote the polarization and energy metabolism of RAW264.7 cells, and participate in positive immune regulation, which is correlated with its enhanced effect of invigorating spleen-stomach and replenishing Qi.

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.

INTRODUCTION: Obstructive sleep apnoea (OSA) is common in Singapore, with moderate to severe OSA affecting around 30% of residents. These consensus statements aim to provide scientifically grounded recommendations for the management of OSA, standar-dise the management of OSA in Singapore and promote multidisciplinary collaboration. METHOD: An expert panel, which was convened in 2024, identified several areas of OSA management that require guidance. The expert panel reviewed the current literature and developed consensus statements, which were later independently voted on using a 3-point Likert scale (agree, neutral or disagree). Consensus (total ratings of agree and neutral) was set a priori at ≥80% agreement. Any statement not reaching consensus was excluded. RESULTS: The final consensus included 49 statements that provide guidance on the screening, diagnosis and management of adults with OSA. Additionally, 23 statements on the screening, diagnosis and management of paediatric OSA achieved consensus. These 72 consensus statements considered not only the latest clinical evidence but also the benefits and harms, resource implications, feasibility, acceptability and equity impact of the recommendations. CONCLUSION The statements presented in this paper aim to guide clinicians based on the most updated evidence and collective expert opinion from sleep specialists in Singapore. These recommendations should augment clinical judgement rather than replace it. Management decisions should be individualised, taking into account the patient's clinical characteristics, as well as patient and caregiver concerns and preferences.
Humans ; Sleep Apnea, Obstructive/diagnosis* ; Singapore ; Consensus ; Adult

Syringa pinnatifolia, belonging to the family Oleaceae, is a species endemic to China. It is predominantly distributed in the Helan Mountains region of Inner Mongolia and Ningxia of China. The peeled roots, stems, and thick branches have been used as a distinctive Mongolian medicinal material known as "Shan-chen-xiang", which has effects such as suppressing "khii", clearing heat, and relieving pain and is employed for the treatment of cardiovascular and pulmonary diseases and joint pain. Over the past five years, significant increase was achieved in research on chemical constituents and pharmacological effects. There were a total of 130 new constituents reported, covering sesquiterpenoids, lignans, and alkaloids. Its effects of anti-myocardial ischemia, anti-cerebral ischemia/reperfusion, sedation, and analgesia were revealed, and the mechanisms of agarwood formation were also investigated. To better understand its medical value and potential of clinical application, this review updates the research progress in recent five years focusing on the chemical constituents and pharmacological effects of S. pinnatifolia, providing reference for subsequent research on active ingredient and support for its innovative application in modern medicine system.
Medicine, Mongolian Traditional ; Humans ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Syringa/chemistry*

OBJECTIVE: To evaluate precise assessment methods for predicting the optimal acetabular cup size in total hip arthroplasty (THA). METHODS: A clinical data of 73 patients (80 hips) who underwent primary THA between December 2022 and July 2024 and met the inclusion criteria was analyzed. There were 39 males and 34 females with an average age of 66.3 years (range, 56-78 years). Among them, 66 cases were unilateral THA and 7 were bilateral THAs. There were 29 patients (34 hips) of osteoarthritis, 35 patients (35 hips) of femoral neck fractures, and 9 patients (11 hips) of osteonecrosis of the femoral head. Based on anteroposterior pelvic X-ray films, three methods were employed to predict acetabular cup size, including preoperative template planning, radiographic femoral head diameter (FHD) measurement, and intraoperative FHD measurement. The predicted acetabular cup sizes from these methods were compared with the actual implanted sizes. RESULTS: The predicted acetabular cup sizes using the preoperative template planning, radiographic FHD measurement, and intraoperative FHD measurement were (51.25±2.81), (49.72±3.11), and (49.90±2.74) mm, respectively, compared to the actual implanted cup size of (50.57±2.74) mm, with no significant difference ( P>0.05). Regarding agreement with the actual implanted cup size, the preoperative template planning achieved exact matches in 35 hips (43.75%), one-size deviation in 41 hips (51.25%), and two-size deviations in 4 hips (5%); the radiographic FHD measurement achieved exact matches in 12 hips (15%), one-size deviation in 57 hips (71.25%), and two-size deviations in 11 hips (13.75%); and the intraoperative FHD measurement achieved exact matches in 26 hips (32.5%), one-size deviation in 52 hips (65%), and two-size deviations in 2 hips (2.5%). There were significant differences in agreement distributions between the three methods and the actual implanted cup sizes ( H=18.579, P<0.001). CONCLUSION The intraoperative FHD measurement, as a simple, cost-effective, and accurate method, effectively guides acetabular cup selection, reduces the risk of prosthesis wear, enhances postoperative joint stability.
Humans ; Arthroplasty, Replacement, Hip/instrumentation* ; Male ; Female ; Middle Aged ; Acetabulum/diagnostic imaging* ; Aged ; Hip Prosthesis ; Prosthesis Design ; Femur Head/surgery* ; Osteoarthritis, Hip/surgery* ; Radiography ; Femoral Neck Fractures/surgery* ; Femur Head Necrosis/surgery*