Objective: To compare the changes in the concentration of relevant growth factors released from platelet-rich plasma (PRP) stored at -80℃ by cryopreservation and at 4℃ by refrigerated lyophilization over 2 years, aiming to provide a theoretical basis for prolonging PRP storage duration. Methods: PRP (n=15) was separated using a blood cell separator and stored under -80℃ cryopreservation (F-PRP group) and 4℃ refrigerated freeze-drying conditions (FD-PRP group). The contents of growth factors (PDGF-AA, PDGF-BB, EGF, TGF-β1, and VEGF) in both groups were measured by ELISA at 1, 3, 6, 9, 12 and 24 months. Results: PDGF-AA and VEGF maintained good stability in both groups for up to 24 months. PDGF-BB and TGF-β1 showed high stability in the first 12 months but their stability decreased gradually from 12th to 24th months. EGF demonstrated good stability in the first 6 months, and its stability gradually decreased from the 9th to 24th months. Comparing the F-PRP and FD-PRP groups, the concentrations of the five growth factors in the FD-PRP group were either not statistically different or higher than those in the F-PRP group at all time points. Specifically, the concentrations of EGF were significantly higher in the FD-PRP group at all time points. Conclusion: Both -80℃ freezing and 4℃ freeze-drying enable long-term preservation of PRP. Freeze-drying imposes less stringent storage requirements and facilitates growth factor compared to frozen storage.

Dormant tumor cells constitute a population of cancer cells that reside in a non-proliferative or low-proliferative state, typically arrested in the G0/G1 phase and exhibiting minimal mitotic activity. These cells are commonly observed across multiple cancer types, including breast, lung, and ovarian cancers, and represent a central cellular component of minimal residual disease (MRD) following surgical resection of the primary tumor. Dormant cells are closely associated with long-term clinical latency and late-stage relapse. Due to their quiescent nature, dormant cells are intrinsically resistant to conventional therapies—such as chemotherapy and radiotherapy—that preferentially target rapidly dividing cells. In addition, they display enhanced anti-apoptotic capacity and immune evasion, rendering them particularly difficult to eradicate. More critically, in response to microenvironmental changes or activation of specific signaling pathways, dormant cells can re-enter the cell cycle and initiate metastatic outgrowth or tumor recurrence. This ability to escape dormancy underscores their clinical threat and positions their effective detection and elimination as a major challenge in contemporary cancer treatment. Hypoxia, a hallmark of the solid tumor microenvironment, has been widely recognized as a potent inducer of tumor cell dormancy. However, the molecular mechanisms by which tumor cells sense and respond to hypoxic stress—initiating the transition into dormancy—remain poorly defined. In particular, the lack of a systems-level understanding of the dynamic and multifactorial regulatory landscape has impeded the identification of actionable targets and constrained the development of effective therapeutic strategies. Accumulating evidence indicates that hypoxia-induced dormancy tumor cells are accompanied by a suite of adaptive phenotypes, including cell cycle arrest, global suppression of protein synthesis, metabolic reprogramming, autophagy activation, resistance to apoptosis, immune evasion, and therapy tolerance. These changes are orchestrated by multiple converging signaling pathways—such as PI3K-AKT-mTOR, Ras-Raf-MEK-ERK, and AMPK—that together constitute a highly dynamic and interconnected regulatory network. While individual pathways have been studied in depth, most investigations remain reductionist and fail to capture the temporal progression and network-level coordination underlying dormancy transitions. Systems biology offers a powerful framework to address this complexity. By integrating high-throughput multi-omics data—such as transcriptomics and proteomics—researchers can reconstruct global regulatory networks encompassing the key signaling axes involved in dormancy regulation. These networks facilitate the identification of core regulatory modules and elucidate functional interactions among key effectors. When combined with dynamic modeling approaches—such as ordinary differential equations—these frameworks enable the simulation of temporal behaviors of critical signaling nodes, including phosphorylated AMPK (p-AMPK), phosphorylated S6 (p-S6), and the p38/ERK activity ratio, providing insights into how their dynamic changes govern transitions between proliferation and dormancy. Beyond mapping trajectories from proliferation to dormancy and from shallow to deep dormancy, such dynamic regulatory models support topological analyses to identify central hubs and molecular switches. Key factors—such as NR2F1, mTORC1, ULK1, HIF-1α, and DYRK1A—have emerged as pivotal nodes within these networks and represent promising therapeutic targets. Constructing an integrative, systems-level regulatory framework—anchored in multi-pathway coordination, omics-layer integration, and dynamic modeling—is thus essential for decoding the architecture and progression of tumor dormancy. Such a framework not only advances mechanistic understanding but also lays the foundation for precision therapies targeting dormant tumor cells during the MRD phase, addressing a critical unmet need in cancer management.

OBJECTIVE To provide a reference for the pharmaceutical care of a patient with type 2 diabetes mellitus （T2DM） and limb-girdle muscular dystrophy （LGMD） who developed euglycemic diabetic ketoacidosis （euDKA） after taking empagliflozin. METHODS Clinical pharmacists provided pharmaceutical care for a patient with T2DM and LGMD who developed euDKA after taking empagliflozin. According to the patient’s recent use of medications and his conditions， clinical pharmacists assessed the correlation between euDKA and empagliflozin as “very likely”. As to euDKA， clinical pharmacists suggested discontinuing empagliflozin and metformin， and giving intravenous infusion of 10% Glucose injection instead of 5% Glucose injection for fluid resuscitation. Clinical pharmacists monitored the patient’s laboratory indicators such as arterial blood gas analysis， blood/urine ketones and electrolytes. They assisted physicians to decide when to stop intravenous supplements of liquid and insulin. Clinical pharmacists also assisted physicians to adjust the antidiabetic drugs and educated the patient to avoid empagliflozin or other sodium- glucose linked transporter 2 inhibitors （SGLT2i）. RESULTS Physicians adopted the suggestions of clinical pharmacists. After treatment， the patient’s condition improved， and he was allowed to be discharged with medication. CONCLUSIONS euDKA is a relatively rare and serious adverse reaction associated with SGLT2i， and the patients with LGMD are susceptible to euDKA. Clinical pharmacists assist physicians in developing personalized medication plans by evaluating the association between euDKA and empagliflozin， adjusting medication regimens，conducting pharmaceutical monitoring，and other pharmaceutical services. Meanwhile， they provide medication education to patients to ensure their medication safety.

OBJECTIVE To provide a reference for the pharmaceutical care of a patient with type 2 diabetes mellitus （T2DM） and limb-girdle muscular dystrophy （LGMD） who developed euglycemic diabetic ketoacidosis （euDKA） after taking empagliflozin. METHODS Clinical pharmacists provided pharmaceutical care for a patient with T2DM and LGMD who developed euDKA after taking empagliflozin. According to the patient’s recent use of medications and his conditions， clinical pharmacists assessed the correlation between euDKA and empagliflozin as “very likely”. As to euDKA， clinical pharmacists suggested discontinuing empagliflozin and metformin， and giving intravenous infusion of 10% Glucose injection instead of 5% Glucose injection for fluid resuscitation. Clinical pharmacists monitored the patient’s laboratory indicators such as arterial blood gas analysis， blood/urine ketones and electrolytes. They assisted physicians to decide when to stop intravenous supplements of liquid and insulin. Clinical pharmacists also assisted physicians to adjust the antidiabetic drugs and educated the patient to avoid empagliflozin or other sodium- glucose linked transporter 2 inhibitors （SGLT2i）. RESULTS Physicians adopted the suggestions of clinical pharmacists. After treatment， the patient’s condition improved， and he was allowed to be discharged with medication. CONCLUSIONS euDKA is a relatively rare and serious adverse reaction associated with SGLT2i， and the patients with LGMD are susceptible to euDKA. Clinical pharmacists assist physicians in developing personalized medication plans by evaluating the association between euDKA and empagliflozin， adjusting medication regimens，conducting pharmaceutical monitoring，and other pharmaceutical services. Meanwhile， they provide medication education to patients to ensure their medication safety.

Objective To explore the clinical characteristics of patients with diffuse large B-cell lymphoma (DLBCL) accompanied with KMT2D gene mutation and the impact of its co-mutated genes on prognosis. Methods Clinical data of 155 newly diagnosed DLBCL patients were obtained. The second-generation sequencing method was used to detect 475 hotspot genes, including KMT2D mutation. Patients were divided into the KMT2D mutation group and KMT2D wild-type group based on the presence or absence of KMT2D gene mutation. Clinical characteristics, differences in co-mutated genes, and survival differences between the two groups were compared. Results The frequency of KMT2D mutation was 31%, which is predominantly observed in elderly patients (P=0.07) and less in the double-expressor phenotype (P=0.07). Compared with the KMT2D wild-type group, KMT2D gene mutation was associated with higher co-mutation rates of CDKN2A (OR=2.82, P=0.01) and BCL2 (OR=3.84, P=0.016), while being mutually exclusive with MYC gene mutation (OR=0.11, P=0.013). In univariate survival analysis, no statistically significant difference in overall survival (OS) was found between the KMT2D mutation group and the wild-type group (P=0.54). Further analysis of the prognostic significance of KMT2D with other gene mutations indicated that patients with KMT2D^mutBTG2^mut had poorer OS than those with KMT2D^wt BTG2^mut (P=0.07) and KMT2D^wt BTG2^wt (P=0.05). On the contrary, patients with KMT2D^mut CD79B^mut had better OS than those with KMT2D^mut CD79B^wt (P=0.09), with no prognostic impact observed for other co-mutated genes. Multivariate Cox regression analysis revealed that Ann Arbor stages Ⅲ and Ⅳ (HR=2.751, 95%CI: 1.169-6.472, P=0.02), elevated LDH levels (HR=2.461, 95%CI: 1.396-4.337, P=0.002), Ki-67 index>80% (HR=1.875, 95%CI: 1.066-3.299, P=0.029), and KMT2D^mutBTG2^mut(HR=4.566, 95%CI: 1.348-15.471, P=0.015) were independent risk factors for OS in patients with DLBCL (P<0.05). Conclusion DLBCL patients with KMT2D mutation often have multiple gene mutations, among which patients with a co-mutated BTG2 gene have poor prognosis.

ObjectiveTo understand the infection characteristics of multidrug-resistant organisms (MDROs) in hospitalized patients in a tertiary sentinel hospital in Shanghai, so as to provide an evidence for the development of targeted prevention and control measures. MethodsData of MDROs strains and corresponding medical records of some hospitalized patients in a hospital in Shanghai from 2021 to 2023 were collected, together with an analysis of the basic information, clinical treatment, underlying diseases and sources of sample collection. ResultsA total of 134 strains of MDROs isolated from hospitalized patients in this hospital were collected from 2021 to 2023 , including 63 strains of methicillin-resistant Staphylococcus aureus (MRSA), 57 strains of carbapenem-resistant Acinetobacter baumannii (CRAB), and 14 strains of carbapenem-resistant Klebsiella pneumoniae (CRKP). Of the 134 strains, 30 strains were found in 2021, 47 strains in 2022 and 57 strains in 2023. The male-to-female ratio of patients was 2.05∶1, with the highest percentage (70.90%) in the age group of 60‒<90 years. The primary diagnosis was mainly respiratory disease, with lung and respiratory tract as the cheif infection sites. There was no statistically significant difference in the distribution of strains between different genders and infection sites (P>0.05). However, the differences in the distribution of strains between different ages and primary diagnosis were statistically significant (P<0.05). Patients who were admitted to the intensive care unit (ICU), had urinary tract intubation, were not artery or vein intubated, were not on a ventilator, were not using immunosuppresants or hormones, and were not applying radiotherapy or chemotherapy were in the majority. There was no statistically significant difference in the distribution of strains for whether received radiotherapy or chemotherapy or not (P>0.05), while the differences in the distribution of strains with ICU admission history, urinary tract intubation, artery or vein intubation, ventilator use, and immunosuppresants or hormones use or not were statistically significant (all P<0.05). The type of specimen was mainly sputum, the hospitalized ward was mainly comprehensive ICU, the sampling time was mainly in the first quarter throughout the year, the number of underlying diseases was mainly between 1 to 2 kinds, the application of antibiotics ≥4 kinds, and those who didn’t receive any surgery recently accounted for the most. There were statistically significant differences in the distribution of strains between different specimen types, wards occupied and history of ICU stay (P<0.05), but no statistically significant difference in the distribution of strains between different sampling times, number of underlying diseases and types of antibiotics applied (P>0.05). ConclusionThe situation of prevention and control on MDROs in this hospital is still serious. Focus should be placed on high-risk factors’ and infection monitoring and preventive measures should be strengthened to reduce the incidence rate of MDROs infection.

Reproductive system diseases are among the primary contributors to the decline in social fertility rates and the intensification of aging, posing significant threats to both physical and mental health, as well as quality of life. Recent research has revealed the substantial potential of the gut microbiota in improving reproductive system diseases. Under healthy conditions, the gut microbiota maintains a dynamic balance, whereas dysfunction can trigger immune-inflammatory responses, metabolic disorders, and other issues, subsequently leading to reproductive system diseases through the gut-gonadal axis. Reproductive diseases, in turn, can exacerbate gut microbiota imbalance. This article reviews the impact of the gut microbiota and its metabolites on both male and female reproductive systems, analyzing changes in typical gut microorganisms and their metabolites related to reproductive function. The composition, diversity, and metabolites of gut bacteria, such as Bacteroides, Prevotella, and Firmicutes, including short-chain fatty acids, 5-hydroxytryptamine, γ-aminobutyric acid, and bile acids, are closely linked to reproductive function. As reproductive diseases develop, intestinal immune function typically undergoes changes, and the expression levels of immune-related factors, such as Toll-like receptors and inflammatory cytokines (including IL-6, TNF-α, and TGF-β), also vary. The gut microbiota and its metabolites influence reproductive hormones such as estrogen, luteinizing hormone, and testosterone, thereby affecting folliculogenesis and spermatogenesis. Additionally, the metabolism and absorption of vitamins can also impact spermatogenesis through the gut-testis axis. As the relationship between the gut microbiota and reproductive diseases becomes clearer, targeted regulation of the gut microbiota can be employed to address reproductive system issues in both humans and animals. This article discusses the regulation of the gut microbiota and intestinal immune function through microecological preparations, fecal microbiota transplantation, and drug therapy to treat reproductive diseases. Microbial preparations and drug therapy can help maintain the intestinal barrier and reduce chronic inflammation. Fecal microbiota transplantation involves transferring feces from healthy individuals into the recipient’s intestine, enhancing mucosal integrity and increasing microbial diversity. This article also delves into the underlying mechanisms by which the gut microbiota influences reproductive capacity through the gut-gonadal axis and explores the latest research in diagnosing and treating reproductive diseases using gut microbiota. The goal is to restore reproductive capacity by targeting the regulation of the gut microbiota. While the gut microbiota holds promise as a therapeutic target for reproductive diseases, several challenges remain. First, research on the association between gut microbiota and reproductive diseases is insufficient to establish a clear causal relationship, which is essential for proposing effective therapeutic methods targeting the gut microbiota. Second, although gut microbiota metabolites can influence lipid, glucose, and hormone synthesis and metabolism via various signaling pathways—thereby indirectly affecting ovarian and testicular function—more in-depth research is required to understand the direct effects of these metabolites on germ cells or granulosa cells. Lastly, the specific efficacy of gut microbiota in treating reproductive diseases is influenced by multiple factors, necessitating further mechanistic research and clinical studies to validate and optimize treatment regimens.

Objective:To explore the effects of hyperoxic environments on renal metabolites to understand the potential mechanisms that contribute to pathologic retinal vascular neovascularization and renal injury through metabolomic studies in a mouse model of oxygen-induced retinopathy (OIR) model.Methods:Sixteen C57/B6J mice pups born to day 7 (P7) were randomly and equally divided into an OIR model group and a normal control group using a randomized numerical table of mother mice.Mice were reared standardly from birth until day 7 (P7), then mice and their mother mice in the OIR group were placed in a hyperoxic (75±2)% chamber until day 12 (P12) and then reared normally.Mice in the normal control group were reared normally throughout.Mice in two groups were killed by carbon dioxide euthanasia on postnatal day 17 (P17). The mice retinal wholemount from the two groups were made and stained with isolectin B4 (IB4) to observe the morphology of retinal vessels, central non-perfusion area and pathological neovascularization.The kidney tissue of P17 mice was analyzed by liquid chromatograph mass spectrometer.After anticoagulant treatment, the whole blood of mice was centrifuged and precipitated, and the obtained plasma without cellular components was analyzed by targeted metabonomics.Mass spectral information was interpreted using metabolomics data processing software Progenesis QI v2.3.Overall differences in metabolic profiles were distinguished by unsupervised principal component analysis and orthogonal partial least squares analysis (OPLS-DA). The fold change and P values of metabolites were compared between the two groups.The variable importance of projection value>1 and P value<0.05 was used to screen out differential metabolites.Metabolic pathway enrichment analysis of differential metabolites was performed based on the KEGG database.The feeding and use of animals were strictly in accordance with the requirements of the Ethics Committee of Jinan University, and the research protocol was reviewed and approved by the Ethics Committee of Jinan University (No.20200401-54). Results:The IB4 staining of retinal wholemounts showed that the retinal blood vessels were evenly distributed in the P17 mice from control group.The peripheral retinal vessels were tortuous and disordered with a large non-perfusion area in central region in P17 mice from OIR group, and a large number of neovascularization clusters were formed at the junction of the nonperfusion area and the vascular area of the retina, showing strong fluorescent staining.The relative area of retinal nonperfusion area in OIR group was (25.16±3.50)%, which was significantly larger than (0.63±0.30)% in normal control group ( t=12.07, P<0.001). The OPLS-DA parameter R2X cum (0.578), interpretation rate R2Y cum (0.978) and prediction rate Q2 cum (0.857) values were all greater than 0.5, indicating that the OPLS-DA model had a good predictive ability.A total of 26 main differential metabolites were found, among which 17 were up-regulated and 9 were down-regulated, including glycerophospholipids (PC 20∶4(5Z, 8Z, 11Z, 14Z)/0∶0, PC 22∶6(4Z, 7Z, 10Z, 13Z, 16Z, 19Z)/0∶0, PC 14∶1(9Z)/20∶2(11Z, 14Z), PE P-18∶0/20∶4(6E, 8Z, 11Z, 14Z)(5OH[S]), amino acid metabolites (arginine, ornithine, pipecolic acid, and hydroxylysine), purines (guanine, hypoxanthine, hydroxypurinol), and fatty acids (methyl 15-palmitate, 2, 6, 8, 12-tetramethyl-2, 4-tridecadien-1-ol), and so on.Differential metabolites were mainly enriched in ABC transporters (L-arginine, taurine, inositol, adenosine, N-acetyl-D-glucosamine, L-glutamine), aminoacyl-tRNA biosynthesis (L-isoleucine, L-proline, L-arginine, L-histidine, L-glutamine), arginine biosynthesis (L-arginine, L-ornithine, L-glutamine) metabolic pathways.The plasma targeted metabonomics showed that the differential amino acid metabolites were mainly enriched in metabolic pathways such as aminoacyl-tRNA biosynthesis, arginine biosynthesis and metabolism, and ABC transporters. Conclusions:ABC transporter, aminoacyl-tRNA biosynthesis, and arginine biosynthesis metabolic pathways in OIR mice may participate in the pathological changes of renal injury and neovascularization in retinopathy of prematurity.

Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of national bloodstream infection Bacterial Resistant Investigation Collaborative System（BRICS）were collected during January 2022 to December 2022. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute（CLSI）. WHONET 5.6 and SPSS 25.0 software were used to analyze the data.Results:During the study period，9 035 strains of Gram-negative bacteria were collected from 51 hospitals，of which 7 895（87.4%）were Enterobacteriaceae and 1 140（12.6%）were non-fermenting bacteria. The top 5 bacterial species were Escherichia coli（ n=4 510，49.9%）， Klebsiella pneumoniae（ n=2 340，25.9%）， Pseudomonas aeruginosa（ n=534，5.9%）， Acinetobacter baumannii complex（ n=405，4.5%）and Enterobacter cloacae（ n=327，3.6%）. The ESBLs-producing rates in Escherichia coli， Klebsiella pneumoniae and Proteus spp. were 47.1%（2 095/4 452），21.0%（427/2 033）and 41.1%（58/141），respectively. The prevalence of carbapenem-resistant Escherichia coli（CREC）and carbapenem-resistant Klebsiella pneumoniae（CRKP）were 1.3%（58/4 510）and 13.1%（307/2 340）；62.1%（36/58）and 9.8%（30/307）of CREC and CRKP were resistant to ceftazidime/avibactam combination，respectively. The prevalence of carbapenem-resistant Acinetobacter baumannii（CRAB）complex was 59.5%（241/405），while less than 5% of Acinetobacter baumannii complex was resistant to tigecycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa（CRPA）was 18.4%（98/534）. There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of main Gram-negative bacteria resistance among different regions，with statistically significant differences in the prevalence of CRKP and CRPA（ χ2=20.489 and 20.252， P<0.001）. The prevalence of CREC，CRKP，CRPA，CRAB，ESBLs-producing Escherichia coli and Klebsiella pneumoniae were higher in provinicial hospitals than those in municipal hospitals（ χ2=11.953，81.183，10.404，5.915，12.415 and 6.459， P<0.01 or <0.05），while the prevalence of CRPA was higher in economically developed regions（per capita GDP ≥ 92 059 Yuan）than that in economically less-developed regions（per capita GDP <92 059 Yuan）（ χ2=6.240， P=0.012）. Conclusions:The proportion of Gram-negative bacteria in bloodstream infections shows an increasing trend，and Escherichia coli is ranked in the top，while the trend of CRKP decreases continuously with time. Decreasing trends are noted in ESBLs-producing Escherichia coli and Klebsiella pneumoniae. Low prevalence of carbapenem resistance in Escherichia coli and high prevalence in CRAB complex have been observed. The composition ratio and antibacterial spectrum of bloodstream infections in different regions of China are slightly different，and the proportion of main drug resistant bacteria in provincial hospitals is higher than those in municipal hospitals.

Objective:To construct and verify a prognostic model based on Necroptosis genes(NEGs)in liver cancer.Methods:Through unsupervised clustering analysis in liver cancer patients from TCGA and ICGC databases,67 NEGs were grouped into two clusters.The differ-ences in prognosis between clusters were explored.Prognosis-related genes were selected through single-factor Cox regression analysis.A prognostic model was built using clustering analy-sis and multi-factor Cox regression,and the model's accuracy and predictive ability were validated.Results:The 67 NEGs were divided into two subtypes,namely NEGclusterA and NEGclusterB.Survival analysis indicated a better prognosis for patients in B compared to A(P＜0.05).Single-factor Cox analysis identified 133 prognosis-related genes,further classified into genecluster A and genecluster B,the prognosis of A was better than B(P＜0.001).Three genes(SLC1A5,MYBL2,and CFHR3)were determined to construct the prognostic risk scoring model.In both TCGA training and validation cohorts,patients in the high-risk group exhibited poorer prognosis(P＜0.05).Conclu-sion:This predictive model can independently forecast the prognosis of liver cancer and provides initial insights into the differences in immune cell infiltration among different liver cancer clusters.