BACKGROUND:Allogeneic hematopoietic stem cell transplantation is an important treatment for malignant hematological diseases,and delayed postoperative platelet implantation is a common complication that seriously affects the quality of patient survival;however,there are no standard protocols to improve platelet implantation rates and prevent platelet implantation delays. OBJECTIVE:To compare the safety and efficacy of oral Herombopag Olamine versus subcutaneous recombinant human thrombopoietin for promoting platelet implantation in patients with malignant hematological diseases undergoing haploid hematopoietic stem cell transplantation. METHODS:Clinical data of 163 patients with malignant hematological diseases who underwent haploidentical hematopoietic stem cell transplantation from January 2016 to October 2022 were retrospectively analyzed.A total of 72 patients who started to subcutaneously inject recombinant human thrombopoietin at+2 days were categorized into the recombinant human thrombopoietin group;a total of 27 patients who started to orally take Herombopag Olamine at+2 days were categorized into the Herombopag Olamine group;and 64 patients who did not apply Herombopag Olamine or recombinant human thrombopoietin were categorized into the blank control group.The implantation status,incidence of acute graft-versus-host disease of degree II-IV within 100 days,1-year survival rate,1-year recurrence rate,and safety were analyzed in the three groups. RESULTS AND CONCLUSION:(1)The average follow-up time was 52(12-87)months.The implantation time of neutrophils in the blank control group,recombinant human thrombopoietin group,and Herombopag Olamine group was(12.95±3.88)days,(14.04±3.71)days,and(13.89±2.74)days,respectively,with no statistically significant difference(P=0.352);the implantation time of platelets was(15.16±6.27)days,(17.67±6.52)days,and(17.00±4.75)days,with no statistically significant difference(P=0.287).(2)The complete platelet implantation rate on day 60 was 64.06%,90.28%,and 92.59%,respectively,and the difference was statistically significant(P<0.001).The subgroup analysis showed that the difference between the blank control group and the recombinant human thrombopoietin group was statistically significant(P<0.001),and the difference between the blank control group and the Herombopag Olamine group was statistically significant(P=0.004).The difference was not statistically significant between the recombinant human thrombopoietin group and Herombopag Olamine group(P=0.535).(3)100-day II-IV degree acute graft-versus-host disease incidence in the blank control group,recombinant human thrombopoietin group,and Herombopag Olamine group were 25.00%,30.56%,and 25.93%,respectively,and the difference was not statistically significant(P=0.752).(4)The incidence of cytomegalovirus anemia,cytomegalovirus pneumonia,and hepatic function injury had no statistical difference among the three groups(P>0.05).(5)During the follow-up period,there was no thrombotic event in any of the three groups of patients.(6)The results showed that recombinant human thrombopoietin and Herombopag Olamine could improve the platelet implantation rate of malignant hematological disease patients after haploidentical hematopoietic stem cell transplantation,with comparable efficacy and good safety.

Background: Hearing loss (HL) is one of the most common sensory disorders, affecting over 5-8% of the world's population. Approximately half of HL cases are attributed to genetic factors. In hereditary deafness, about 75-80% is inherited through autosomal recessive inheritance, and common pathogenic genes include GJB2 and SLC26A4. Pathogenic variants in the SLC26A4gene are the leading cause of hereditary hearing loss in humans, second only to the GJB2 gene. Variants in the SLC26A4gene cause hearing loss, which can be non-syndromic autosomal recessive deafness (DFNB4, OMIM #600791) associated with enlarged vestibular aqueduct (EVA) or Pendred syndrome (Pendred, OMIM #605646). DFNB4 is characterized by sensorineural hearing loss combined with EVA or less common cochlear malformation defect. Pendred syndrome is characterized by bilateral sensorineural hearing loss with EVA and an iodine defect that can lead to thyroid goiter. Currently, it is known that EVA is associated with variants in the SLC26A4 gene and is a penetrant feature of SLC26A4-related HL. Predominant mutations in these genes differ significantly across populations. For instance, predominant SLC26A4 mutations differ among populations, including p.T416P and c.1001G>A in Caucasians, p.H723R in Japanese and Koreans, and c.919-2A>G in Han Taiwanese and Han Chinese. On the other hand, there has been no study of hearing loss related to SLC26A4 gene variants among Mongolians, which is the basis of our research. Aim: We aimed to identify the characteristics of the SLC26A4 gene variants in Mongolian people with Enlarged vestibular aqueduct and Mondini malformation. Materials and Methods: In 2022-2024, We included 13 people with hearing loss and enlarged vestibular aqueduct, incomplete cochlea (1.5 turns of the cochlea with cystic apex- incomplete partition type II- Mondini malformation) were examined by CT scan of the temporal bone in our study. WES (Whole exome sequencing) analysis was performed in the Genetics genetic-laboratory of the National Taiwan University Hospital. Results: Genetic analysis revealed 26 confirmed pathogenic variants of bi-allelic SLC26A4 gene of 8 different types in 13 cases, and c.919-2A>G variant was dominant with 46% (12/26) in allele frequency, and c.2027T>A (p.L676Q) variant 19% (5/26), c.1318A>T(p.K440X) variant 11% (3/26), c.1229C>T (p.T410M) variant 8% (2/26) ) , c.716T>A (p.V239D), c.281C>T (p.T94I), c.1546dupC, and c.1975G>C (p.V659L) variants were each 4% (1/26)- revealed. Two male children, 11 years old (SLC26A4: c.919-2A>G) and 7 years old (SLC26A4: c.919-2A>G:, SLC26A4: c.2027T>A (p.L676Q))had history of born normal hearing and progressive hearing loss. Conclusions 1. 26 variants of bi-allelic SLC26A4 gene mutation were detected in Mongolian people with EVA and Mondini malformation, and c.919-2A>G was the most dominant allele variant, and rare variants such as c.1546dupC, c.716T>A (p.V239D) were detected. 2. Our study shows that whole-exome sequencing (WES) can identify gene mutations that are not detected by polymerase chain reaction (PCR) or NGS analysis.

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.

AIM: To assess the consistency of the new anterior segment analyzer, MS-39, the Sirius and Pentacam in measuring corneal white-to-white(WTW)and central anterior chamber depth(ACD), and to compare their differences in guiding implantable collamer lens(ICL)size selection.METHODS: Retrospective case study. A total of 210 consecutive patients(420 eyes)who treated at the Ophthalmology Refractive Surgery Center of the First Affiliated Hospital of Xi'an Jiaotong University between September 2019 and September 2020 were enrolled. Three anterior segment analysis systems, MS-39, Sirius, and Pentacam, were utilized to assess the WTW and ACD, with comparative analysis of the results. The sizing of the ICL V4c was simulated using the method recommended by the STAAR company. Data correlation and consistency were evaluated.RESULTS: The WTW measurement results obtained from MS-39, Sirius, and Pentacam were 11.39±0.35, 11.42±0.36, and 11.46±0.35 mm, respectively. Notably, the WTW measurement value from MS-39 was significantly lower than that from Pentacam(P=0.002), while no statistically significant differences were observed between MS-39 and Sirius, or between Sirius and Pentacam(all P>0.05). The WTW measurements from the three devices exhibited a strong positive correlation, with correlation coefficients(r)of 0.942 between MS-39 and Sirius, 0.925 between MS-39 and Pentacam, and 0.882 between Sirius and Pentacam(all P<0.0001). The ACD measurements values from the MS-39, Sirius and Pentacam were 3.28±0.22, 3.28±0.24, and 3.21±0.23 mm, respectively. While, no statistically significant difference was found between MS-39 and Sirius(P>0.05), both measurements were significantly higher than that of Pentacam(both P<0.0001). The ACD measurements also demonstrated a strong positive correlation, with r values of 0.959 between MS-39 and Sirius, 0.947 between MS-39 and Pentacam, and 0.932 between Sirius and Pentacam(all P<0.0001). In terms of ICL size selection based on the measurements from the three devices, the 12.6 mm size was the most frequently selected, while the 13.7 mm size was the least common, the distribution of size selections across the devices was similar.CONCLUSION: MS-39 demonstrated strong positive correlation with both Sirius and Pentacam for WTW and ACD measurements, indicating that the results can be considered clinically interchangeable. Furthermore, the outcomes derived from MS-39 for ICL size selection were closely aligned with those from Sirius and Pentacam, suggesting its clinical feasibility.

Objective: To explore the prevalence of screening myopia and depressive symptom among primary and secondary school students in Xuzhou, and to explore the association of sleep and screen time on the coexistence of screening myopia and depressive symptom, so as to provide scientific references for developing intervention strategies to address the development of myopia and promote mental health in children and adolescents. Methods: From September to October 2024, a stratified cluster random sampling method was used to select 6 605 students in grade 4 to 12 in 2 urban and 2 suburban districts in Xuzhou. The students health condition and influencing factors questionnaire were used to assess students basic information, sleep time, and screen time. The Center for Epidemiological Studies Depression Scale (CES-D) was used to assess primary and secondary school students depressive symptom.Unaided distance visual acuity examination was conducted, and refractive assessment was performed using an automated refractometer without cycloplegic agents. The Chi-square test and multiple Logistic regression analysis were used to evaluate the association of sleep and screen time with the coexistence of screening myopia and depressive symptom. Results: The detection rates of screening myopia, depressive symptom, and screening myopia and depressive symptoms co morbidity among primary and secondary school students in Xuzhou were 60.35%, 4.45% and 18.61% respectively. Results from the multinomial Logistic regression analysis, using the healthy group as the reference and after adjusting for confounding factors, showed that students with insufficient sleep duration were more likely to have depressive symptom ( OR=1.57, 95%CI =1.08-2.27) and the coexistence of screening myopia and depressive symptom ( OR=1.85, 95%CI =1.45-2.36). Students with daily screen time≥2 h were more likely to have depressive symptom only ( OR=1.41, 95%CI =1.04-1.93) and the coexistence of screening myopia and depressive symptom ( OR=1.31, 95%CI =1.06-1.61). Further stratified analysis based on sufficient and insufficient sleep duration revealed that only in the insufficient sleep duration group, students with daily screen time≥2 h had an increased risk of depressive symptom only ( OR=1.49, 95%CI =1.07-2.07) and the coexistence of screening positive myopia and depressive symptom ( OR=1.40, 95%CI =1.11- 1.77 ) (all P <0.05). Conclusions Primary and secondary school students with insufficient sleep duration and daily screen time≥2 h have higher risks of depressive symptoms and the coexistence of screening myopia and depressive symptoms. It is recommended to ensure adequate sleep duration and limit screen time for children and adolescents.

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