In recent years,significant progress has been made in diabetes research both domestically and internationally,new diagno-sis and treatment techniques and methods have been constantly emerging,and clinical research evidence has been continuously accu-mulated and updated.To keep pace with these important developments,the Diabetes Branch of the Chinese Medical Association has ac-tively organized experts to update the China Guidelines for the Prevention and Treatment of Diabetes.This update aims to provide a more comprehensive and scientific guide for diabetes prevention and treatment,greatly promote the standardized and integrated man-agement of diabetes by clinicians,and ensure that patients receive standardized and personalized treatment plans to improve therapeu-tic outcomes and quality of life.

Over the years,the American Diabetes Association(ADA)has been actively committed to the development and promotion of standards for the diagnosis,treatment,and daily care of diabetes.Since 1989,it has updated the diabetes diagnosis and treatment standards every year,which have become one of the most authoritative guidelines in diabetes and have been recognized and adopted by various countries.On December 10,2024,the 2025 Standards of Care in Diabetes were released,incorporating the latest evidence-based medicine content related to diabetes and its complications and comorbidities.It aims to provide guidance on the diagnosis,treat-ment,and management of the condition for clinicians,patients and their families,and researchers.This article interprets the major up-dates from the Standards.

With the acceleration of population aging in China,the management of elderly patients with diabetes has become a critical issue in the field of public health.By integrating international evidence-based medical research data with the specific conditions of China,the 2024 edition of Guideline for the management of diabetes mellitus in the elderly in China proposes individualized diagnosis and treatment strategies for elderly patients with diabetes.This article gives a systematic interpretation of the guideline from a nursing perspective,with a focus on the core contents closely associated with nursing practice,and it also elaborates on the implications for nursing work,summarizes and analyzes the challenges in the management of elderly patients with diabetes,and proposes corresponding strategies,in order to provide a theoretical basis and practical guidance for implementing nursing management of elderly patients with diabetes among clinical nurses.

The Guideline for the prevention and treatment of diabetes mellitus in China(2024 edition),released by the Chinese Dia-betes Society on January 20,2025,has updated evidence on diabetes mellitus from various aspects including its epidemiological status in China,diagnosis and treatment progress,and complication management,aiming to guide and facilitate standardized comprehensive management of diabetes mellitus in clinical practice.This paper interprets the guideline from the perspective of nursing practice,focus-ing on nursing care for special diabetic conditions,lifestyle and behavioral interventions,and the procedures of relevant nursing tech-niques.We hope to provide nursing professionals with standardized guidance on diabetes prevention and treatment,thereby further standardizing and refining specialized nursing care for diabetes mellitus,enhancing the quality of patient care,and improving patient prognosis.

Objective To investigate the distribution of A subgroups in the A and AB blood type populations among voluntary blood donors in Zhongshan,study the antigen-antibody characteristics of A subgroups,establish a local A subgroup database,and support the development of precision medicine.Methods ABO subgroup screening was performed using the microplate method.Specimens negative for monoclonal anti-A1 reactivity underwent sequencing of exons 1～7 of the ABO gene to confirm genotypes.Cryopreservation and thawing of glycerolized subgroup red blood cells(RBCs),as well as preservation efficacy of concentrated human-derived antibodies with preservatives,were studied.Results Among 1 212 blood donor specimens,28 subgroup specimens were identified,with a prevalence of 1.54%(15/971)in blood type A and 5.39%(13/241)in blood type AB.Sequencing of 10 specimens revealed 7 ABO genotypes:ABO*A2.01/O.01.02(2 cases),ABO*A1.02/B.01(3 cases),ABO*BA.02/O.01.02,ABO*AW.31.02-05/A2.05,ABO*A2.05/B.01,ABO*A2new/O.01.01,and ABO*A1.02/O.01.01(1 case each).Additionally,one rare allele mutation(c.700C>G)and one novel allele mutation(c.203G>T)(GenBank accession number:PQ152337)were identified.Human anti-A1 antibodies with a titer of 8 were successfully concentrated.Optimal preservation conditions included 0.1%preservative concentration and cryopreserved subgroup RBCs stored at 4℃for 3 days post-thaw.Conclusion The predominant A subgroups in Zhongshan donors are A2 and B(A).A preliminary database for A2 and A2B subgroups is established,along with the discovery of a novel ABO allele mutation.Cryopreservation with glycerol,PEG antibody concentration,and ProClin 300 preservative demonstrate effective applications in preserving ABO blood group antigens and antibodies.

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.

Objective:To compare the embryo development potential and clinical outcomes between the patients with and without oocyte degeneration.Methods:This retrospective cohort study included a total of 242 cycles underwent ICSI that cultured in time-lapse incubator from January 2019 to June 2023 at the Reproductive and Genetic Center of Suzhou Municipal Hospital and all 3 119 oocytes were evaluated. Data collection continued to February 5th,2024 until the last birthing of the study. Patients were divided into degenerated group (140 cycles) and control group (102 cycles) according to whether oocyte degenerated after ICSI. Then the embryo developmental potential and clinical outcomes were compared. Furthermore, we also investigated whether embryo morphokinetics could be different between the two groups.Results:Female age, duration of infertility, body mass index, basal follicle sitmulating hormone, basal luteinizing hormone, basal estrogen (E 2), antral follicle count, anti-Müllerian hormone, factors of infertility and source of semen were similar between the two groups ( P>0.05). E 2 on human chorionic gonadotropin triggered day [2 513.00 (1 842.20, 3 638.50) ng/L], number of oocytes retrieved (13.56±4.80) and oocyte maturation rate [84.35% (1 601/1 898)] were significantly higher in degenerated group than those in control group [2 270.50 (1 472.00, 3 044.20) ng/L, P=0.019; 11.97±4.71, P=0.011; 81.08% (990/1 221), P=0.017], while normal fertilization rate [69.33% (1 103/1 591)], day 3 (D3) good-quality embryos [57.85% (634/1 096)], blastocyst formation rate [50.87% (469/922)] and embryo utilized rate [58.30% (643/1 103)] were significantly lower in degenerated group than those in control group [85.56% (847/990), P<0.001; 65.72% (556/846), P<0.001; 61.26% (446/728), P<0.001; 66.12% (560/847), P<0.001] . In addition, the proportion of low cell number (<7) of D3 embryos [33.76% (370/1 096)] and high fragmentation (fragmentation ≥50%, fragmentation 20%-50%) of D3 embryos [10.01% (109/1 089), 18.64% (203/1 089)] in degenerated group were significantly higher than those in control group [27.19% (230/846), P=0.002; 6.06% (51/841), P=0.002; 14.15% (119/841), P=0.009], and so were the incidence of DC1 and CC [5.98% (66/1 103) vs. 2.48% (21/847), P<0.001; 2.45% (27/1 103) vs. 0.94% (8/847), P=0.013]. As regard to the utilized embryos, there were no significant differences in t5, cc2, cc3 and s2 ( P>0.05), but tPNf [22.82(21.13, 24.84) h], t2 [25.37 (23.62, 27.37) h], t3 [35.64 (33.10, 38.03) h] and t4 [36.85 (34.70, 39.52) h] in degenerated group were significantly earlier than those in control group [23.04 (21.76, 25.41) h, P=0.001; 25.91 (24.15, 28.05) h, P=0.001; 36.16 (33.11, 38.81) h, P=0.040; 37.39 (35.11, 40.27) h, P=0.026]. Further more, after the first transfer of fresh or frozen embryos, there were no significant differences in clinical pregnancy rate, implantation rate, early abortion rate, live birth rate, sex ratio, preterm birth rate, low birth weight rate and birth defect rate between the two groups (all P>0.05). ICSI degeneration was not an independent factor of implantation rate, early abortion rate and live birth rate after ICSI treatment, but number of embryos transferred was an independent factor of implantation rate and live birth rate after ICSI treatment ( OR=2.806, 95% CI: 1.179-6.677, P=0.020; OR=2.622, 95% CI: 1.129-6.090, P=0.025). Conclusion:The presence of oocyte degeneration after ICSI may affect the overall developmental potential of its sibling oocytes and may also disturb the morphokinetics of the embyos, however the pregnancy outcomes and neonatal birth outcomes may not be affected if transfer the best embryo in the first fresh or frozen cycle.

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.