ObjectiveThis study sought to investigate the impact of exosomes derived from LoVo cells (LoVo-Exos) in colorectal cancer (CRC) on tumor angiogenesis, as well as to elucidate the potential molecular mechanisms underlying their pro-angiogenic effects. MethodsLoVo-Exos were isolated via ultracentrifugation, and their internalization into recipient human umbilical vein endothelial cells (HUVECs) was visualized using confocal microscopy. The influence of LoVo-Exos on angiogenesis was assessed through an in vitro tube formation assay. Additionally, the pro-angiogenic effects of LoVo-Exos were evaluated in vivo using a matrix gluing assay in mice. To investigate the molecular mechanisms through which LoVo-Exos facilitate angiogenesis, Western blot analysis was employed to examine the transfer of pEGFR by LoVo-Exos into recipient cells. Both Western blot and ELISA were utilized to assess the expression levels of key signaling proteins within the EGFR-ERK pathway, as well as the expression of downstream angiogenic core molecules. Furthermore, the impact of EGFR knockdown and ERK inhibitor treatment on angiogenesis was evaluated, with subsequent analysis of the expression of downstream angiogenic core molecules following these interventions. ResultsConfocal microscopy demonstrated the internalization of LoVo-Exos into HUVECs. In vitro angiogenesis assays further indicated that LoVo-Exos significantly enhanced the formation of tubular structures in HUVECs. Additionally, macroscopic examination of subcutaneous matrix plug formation in mice revealed a substantial increase in vascular-like structures within the matrix plugs following the administration of LoVo-Exos, compared to the PBS control group. Hematoxylin and eosin (HE) staining revealed the presence of erythrocyte-filled microvessels within the matrix plugs combined with LoVo-Exos. Furthermore, immunohistochemical analysis demonstrated the expression of the endothelial cell marker CD31 in these matrix plugs. The presence of CD31-positive cells in the LoVo-Exos-treated matrix plugs was associated with a significant enhancement in the formation of luminal structures. These findings suggest that LoVo-Exos facilitate the in vivo development of vascular-like structures. Subsequent investigations demonstrated that LoVo-Exos facilitated the delivery of pEGFR to HUVEC, thereby enhancing angiogenesis. Conversely, LoVo-Exos with EGFR knockdown exhibited a diminished capacity to promote angiogenesis, an effect that was further attenuated by the ERK phosphorylation inhibitor U0126. Western blot analysis assessing the activation of the EGFR-ERK signaling pathway in HUVEC indicated that LoVo-Exos augmented angiogenesis through the activation of this pathway. Furthermore, analysis of the impact of LoVo-Exos on the expression of downstream angiogenic core molecules revealed an increase in interleukin-8 (IL-8) secretion in HUVEC. The enhancement observed was diminished in LoVo-Exos following EGFR knockdown, and this reduction was counteracted by the ERK phosphorylation inhibitor U0126. ConclusionThe underlying mechanism may involve the delivery of pEGFR in LoVo-Exos to HUVECs, leading to increased IL-8 secretion via the EGFR-ERK signaling pathway, thereby enhancing the angiogenic potential of HUVECs. This finding may offer new insights into the mechanisms underlying cancer metastasis.

OBJECTIVES: To explore the measures to improve the follow-up rate of preterm infants after discharge, and to evaluate the effectiveness of these measures using quality improvement methodology. METHODS: The follow-up status of preterm infants discharged from March to May 2017 was used as the baseline before quality improvement, and a specific quality improvement goal for the follow-up rate was proposed. The Pareto chart was used to analyze the causes of follow-up failure, and a key driver diagram was constructed based on the links involved in improving follow-up rate. The causes of failure were analyzed to determine the key links and intervention measures for quality improvement, and the follow-up rate was monitored weekly using a control chart until the quality improvement goal was achieved. RESULTS: The follow-up rate of preterm infants after discharge was 57.92% (117/202) at baseline before quality improvement, and the quality improvement goal was set to increase the follow-up rate of preterm infants from baseline to more than 80% within 12 months. The Pareto chart analysis showed that the main causes of follow-up failure were deficiencies in follow-up file management and irregular follow-up times (33.70%, 31/92), insufficient follow-up education and poor communication (25.00%, 23/92), and the inability to meet the diverse needs of parents (18.48%, 17/92). Based on the key links for quality improvement and the main causes of follow-up failure, the following intervention measures were adopted: (1) strengthen follow-up publicity and education; (2) build a follow-up team; and (3) establish a follow-up platform and system. The control chart indicated that with the implementation of the above intervention measures, the weekly follow-up rate increased to 74.09% (306/413) in July 2017 and 83.09% (511/615) in December 2017, finally achieving the quality improvement goal. During the COVID-19 pandemic, the follow-up rate of preterm infants fluctuated between 23.54% (460/1 954) and 70.97% (1 931/2 721), and subsequently, it returned to pre-pandemic levels starting in February 2023. CONCLUSIONS The application of quality improvement methodology can help to formulate intervention measures based on the main causes of follow-up failure, thereby improving the follow-up rate of preterm infants after discharge. This quality improvement method is feasible and practical and thus holds promise for clinical application.
Humans ; Quality Improvement ; Infant, Premature ; Infant, Newborn ; Patient Discharge ; Follow-Up Studies ; Female ; Male

OBJECTIVES: To explore the application of the colloidal gold method and chemiluminescence method in detecting gonadotropin (Gn) in morning urine for assessing pubertal development status in children. METHODS: A total of 132 children diagnosed with central precocious puberty (CPP), early and fast puberty (EFP), and premature thelarche (PT) at Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from November 2021 to December 2022 were included, along with 685 healthy children who underwent routine health examinations at the hospital's pediatric health care department during the same period. All 132 patients underwent a gonadotropin-releasing hormone (GnRH) stimulation test. Both patients and healthy children had their urinary Gn levels measured using the colloidal gold method and chemiluminescence method, including levels of luteinizing hormone (LH) and follicle-stimulating hormone (FSH). The correlation between serum Gn and urinary Gn detected by the two methods, as well as the correlation between Tanner stages of healthy children and urinary Gn, was analyzed. RESULTS: Urine Gn levels detected by both the colloidal gold method and chemiluminescence method showed a positive correlation with serum LH baseline values, LH peak values, baseline LH/FSH ratios, and peak LH/FSH ratios (P<0.05). In healthy children, urinary LH levels detected by the chemiluminescence method gradually increased from Tanner stage Ⅰ to Ⅳ (P<0.05), while urinary FSH levels were lower in Tanner stage I than in stages Ⅱ, Ⅲ, and IV (P<0.05). Urinary LH levels detected by the colloidal gold method were lower in Tanner stage I compared to stages Ⅱ, Ⅲ, and IV, with the highest levels observed in Tanner stage Ⅳ (P<0.05). Additionally, urinary FSH levels in Tanner stage Ⅲ were higher than in stages Ⅰ and Ⅱ (P<0.05). The area under the receiver operating characteristic curve for evaluating Tanner stages I and II in healthy children using urinary LH and FSH levels by the chemiluminescence method and urinary LH levels by the colloidal gold method were 0.730, 0.699, and 0.783, respectively. CONCLUSIONS The colloidal gold method and chemiluminescence method for detecting Gn in morning urine show good correlation with serum Gn levels. As a non-invasive and convenient detection method, the colloidal gold method can serve as a useful tool for screening the onset of pubertal development in children.
Humans ; Child ; Male ; Female ; Gold Colloid ; Luminescent Measurements/methods* ; Gonadotropins/urine* ; Puberty ; Luteinizing Hormone/urine* ; Child, Preschool ; Adolescent ; Follicle Stimulating Hormone/urine*

OBJECTIVE: To explore the effect of simvastatin monotherapy or in combination with doxorubicin on diffuse large B-cell lymphoma (DLBCL) cells and its possible molecular mechanisms. METHODS: The differences in the expression levels of genes and proteins related to the mevalonate (MVA) pathway between DLBCL tissues and reactive lymph node hyperplasia tissues were compared via database analysis, as well as their effects on the prognosis. CCK-8 assay was used to detect the effect of simvastatin and doxorubicin on the viability of different subtypes of DLBCL cells, EdU was used to detect cell proliferation, flow cytometry was used to detect apoptosis, and Western blot was used to detect related protein and signaling pathway proteins. RESULTS: The expression levels of MVA pathway-related genes were increased in tumor tissues of DLBCL patients through the TCGA database, and the median overall survival time of DLBCL patients in HMGCR high expression group was shorter (all P < 0.05). Meanwhile, according to The Human Protein Atlas database, HMGCR protein was significantly high expressed in DLBCL tumor tissue compared with normal tissue. The viability of DLBCL cell lines treated with simvastatin or doxorubicin monotherapy was decreased in time- and concentration-dependent manner, and could be further inhibited by simvastatin combined with doxorubicin especially in GCB subtype cell lines. Both simvastatin and doxorubicin could inhibit the proliferation of DLBCL cell lines, and their combination further suppressed dramatically. Both the two drugs promoted apoptosis in DLBCL cell lines, and the apoptosis was further increased after their combination. Compared with monotherapy, the expression of HMGCR protein and apoptosis-related protein Bcl-2 was further decreased but cleaved-caspase3 and Bax increased after combination therapy. Meanwhile, the expression level of phosphorylated proteins in PI3K-Akt pro-survival signaling pathway were decreased especially in GCB subtype cell lines. CONCLUSION HMGCR, the protein associated with cholesterol synthesis pathway, is highly expressed in DLBCL tumor tissues and indicates poor prognosis. Simvastatin, a lipid-lowering drug, combined with doxorubicin can further affect the survival of DLBCL tumor cells at the cellular level.
Humans ; Lymphoma, Large B-Cell, Diffuse/metabolism* ; Doxorubicin/pharmacology* ; Simvastatin/pharmacology* ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Signal Transduction ; Cell Line, Tumor ; Hydroxymethylglutaryl CoA Reductases/metabolism*

OBJECTIVE: To screen novel diagnostic marker or therapeutic target for multiple myeloma (MM). METHODS: Sel1L, SPAG4, KCNN3 and PARM1 were identified by bioinformatics method based on GEO database as high expression genes in MM. Their RNA and protein expression levels in bone marrow mononuclear cells from myeloma cell lines U266, NCI-H929, MM.1s, RPMI8226 and leukemia cell line THP1, as well as 31 MM patients were evaluated by RT-PCR and Western blot, respectively. Meanwhile, 5 samples of bone marrow from healthy donors for allogeneic hematopoietic stem cell transplantation were employed as controls. RESULTS: Compared with leukemia cell line THP1, the expression levels of KCNN3, PARM1 and Sel1L mRNA were significantly increased in myeloma cell lines U266, NCI-H929 and MM.1s, while PARM1 was further increased in myeloma cell lines 8226. Western blot showed that the 4 genes were all expressed in the 4 myeloma cell lines. Compared with healthy controls, the expression levels of Sel1L, SPAG4, KCNN3 and PARM1 mRNA were significantly higher in MM patients (all P < 0.05). Western blot showed that the 4 genes were all expressed in MM patients, and the protein expression level of Sel1L and KCNN3 were significantly different compared with healthy donors (all P < 0.01). CONCLUSION Sel1L, SPAG4, KCNN3 and PARM1 may be potential diagnostic markers and therapeutic targets for MM.
Humans ; Multiple Myeloma/genetics* ; Cell Line, Tumor ; Proteins/metabolism* ; Computational Biology ; RNA, Messenger/genetics*

OBJECTIVE: To investigate the predictive value of serum soluble interleukin-2 receptor(sIL-2R), tumor necrosis factor alpha(TNF-α), IgG and IgA for the recurrence in patients with multiple myeloma(MM). METHODS: A total of 108 MM patients who were initially diagnosed and treated in our hospital from January 2017 to March 2019, and 72 patients who met the diagnostic criteria and had complete follow-up data were selected as the study subjects. MM recurrence was the endpoint event, and follow-up was conducted until the occurrence of the endpoint event or the deadline of this study. MM patients were divided into recurrent group(RG) and non-recurrent group(NRG) based on whether they have relapsed or not. Venous blood was collected from patients at the first diagnosis and follow-up (at the occurrence of endpoint events or termination of the study), and enzyme-linked immunosorbent assay(ELISA) was used to detect sIL-2R and TNF-α levels in the patient's serum. An automatic immune analyzer was used to detect the levels of IgG and IgA in the patient's serum. The differences in expression levels of the factors between two groups were compared and the correlations between sIL-2R and TNF-α, IgG and IgA at the first diagnosis and follow-up were analyzed. At the same time, venous blood was collected from patients during complete remission, and their serum sIL- 2R levels were measured to compare the differences in sIL-2R expression levels at the first diagnosis, complete remission and recurrence. Receiver operating characteristic(ROC) curves was used to determine the optimal cutoff values for serum sIL-2R, TNF-α, IgG and IgA, and the predictive value of sIL-2R, TNF-α, IgG and IgA in the recurrence of MM patients were analyzed based on the area under the curve(AUC). RESULTS: The serum sIL-2R levels of MM patients at the first diagnosis and recurrence were significantly higher than at complete remission (P < 0.05). At the first diagnosis, the hemoglobin content of RG was lower than that of NRG, while the β₂-microglobulin content was higher than that of NRG (P < 0.001). There was no significant difference in other clinical parameters between the two groups (P >0.05). The levels of sIL-2R, TNF-α, IgG and IgA at the first diagnosis and follow-up of RG were higher than those of NRG (P < 0.05). There was a significant correlation between sIL-2R and TNF-α, IgG and IgA at the first diagnosis and follow-up (P < 0.001). The ROC curve showed that, at the first diagnosis, sIL-2R, TNF-α, IgG and IgA predicted the AUC of MM patients were 0.919, 0.850, 0.766 and 0.795, respectively, after follow-up, they predicted AUC of MM were 0.890, 0.815, 0.760 and 0.794, respectively (P < 0.001). CONCLUSION The serum sIL-2R has the highest predictive value for MM patient's recurrence, and it is possible to detect the TNF-α, IgG and IgA levels at specific times to infer changes in sIL-2R levels and evaluate the patient's prognosis.
Humans ; Multiple Myeloma/blood* ; Immunoglobulin A/blood* ; Immunoglobulin G/blood* ; Tumor Necrosis Factor-alpha/blood* ; Receptors, Interleukin-2/blood* ; Recurrence ; Male ; Female ; Neoplasm Recurrence, Local ; Middle Aged ; Prognosis

Sleep deprivation (SD) has emerged as a significant modifiable risk factor for Alzheimer’s disease (AD), with mounting evidence demonstrating its multifaceted role in accelerating AD pathogenesis through diverse molecular, cellular, and systemic mechanisms. SD is refined within the broader spectrum of sleep-wake and circadian disruption, emphasizing that both acute total sleep loss and chronic sleep restriction destabilize the homeostatic and circadian processes governing glymphatic clearance of neurotoxic proteins. During normal sleep, concentrations of interstitial Aβ and tau fall as cerebrospinal fluid oscillations flush extracellular waste; SD abolishes this rhythm, causing overnight rises in soluble Aβ and tau species in rodent hippocampus and human CSF. Orexinergic neurons sustain arousal, and become hyperactive under SD, further delaying sleep onset and amplifying Aβ production. At the molecular level, SD disrupts Aβ homeostasis through multiple converging pathways, including enhanced production via beta-site APP cleaving enzyme 1 (BACE1) upregulation, coupled with impaired clearance mechanisms involving the glymphatic system dysfunction and reduced Aβ-degrading enzymes (neprilysin and insulin-degrading enzyme). Cellular and histological analyses revealed that these proteinopathies are significantly exacerbated by SD-induced neuroinflammatory cascades characterized by microglial overactivation, astrocyte reactivity, and sustained elevation of pro-inflammatory cytokines (IL-1β, TNF-α, IL-6) through NF‑κB signaling and NLRP3 inflammasome activation, creating a self-perpetuating cycle of neurotoxicity. The synaptic and neuronal consequences of chronic SD are particularly profound and potentially irreversible, featuring reduced expression of critical synaptic markers (PSD95, synaptophysin), impaired long-term potentiation (LTP), dendritic spine loss, and diminished neurotrophic support, especially brain-derived neurotrophic factor (BDNF) depletion, which collectively contribute to progressive cognitive decline and memory deficits. Mechanistic investigations identify three core pathways through which SD exerts its neurodegenerative effects: circadian rhythm disruption via BMAL1 suppression, orexin system hyperactivity leading to sustained wakefulness and metabolic stress, and oxidative stress accumulation through mitochondrial dysfunction and reactive oxygen species overproduction. The review critically evaluates promising therapeutic interventions including pharmacological approaches (melatonin, dual orexin receptor antagonists), metabolic strategies (ketogenic diets, and Mediterranean diets rich in omega-3 fatty acids), lifestyle modifications (targeted exercise regimens, cognitive behavioral therapy for insomnia), and emerging technologies (non-invasive photobiomodulation, transcranial magnetic stimulation). Current research limitations include insufficient understanding of dose-response relationships between SD duration/intensity and AD pathology progression, lack of long-term longitudinal clinical data in genetically vulnerable populations (particularly APOE ε4 carriers and those with familial AD mutations), the absence of standardized SD protocols across experimental models that accurately mimic human chronic sleep restriction patterns, and limited investigation of sex differences in SD-induced AD risk. The accumulated evidence underscores the importance of addressing sleep disturbances as part of multimodal AD prevention strategies and highlights the urgent need for clinical trials evaluating sleep-focused interventions in at-risk populations. The review proposes future directions focused on translating mechanistic insights into precision medicine approaches, emphasizing the need for biomarkers to identify SD-vulnerable individuals, chronotherapeutic strategies aligned with circadian biology, and multi-omics integration across sleep, proteostasis and immune profiles may delineate precision-medicine strategies for at-risk populations. By systematically examining these critical connections, this analysis positions sleep quality optimization as a viable strategy for AD prevention and early intervention while providing a comprehensive roadmap for future mechanistic and interventional research in this rapidly evolving field.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

OBJECTIVE To explore a new model of intelligent medication education for pharmaceutical outpatient clinics by constructing dynamic HTML web pages through the DeepSeek-V3-0324 large-scale model. METHODS Clinical pharmacists integrated key clinical information such as patients’ basic information， medication history and medication precautions in real time， and generated a standardized medication education list through the DeepSeek-V3-0324 large-scale model and manual review. RESULTS The DeepSeek-V3-0324 large-scale model was applied in the pharmaceutical outpatient clinics to generate a personalized medication education list， which could effectively solve the disunity of pharmacy guidance caused by the lack of standardization of medication education and the difference of individualized experience of pharmacists in the traditional pharmaceutical outpatient clinics in the face of complex cases， and medication errors caused by forgetting or misremembering information among certain special patient populations after receiving medication education. CONCLUSIONS The transformation and application of artificial intelligence technology in pharmaceutical outpatient clinics is an innovation of pharmaceutical outpatient service means， which can provide patients with immediate and personalized medication education and improve the quality of pharmaceutical care. However， it is also necessary to face the lag of database update and the lack of risk management， as well as the lack of diversification of medication education lists.

A scoping review was performed to systematically search and summarize the clinical research in the treatment of dysmenorrhea with oral Chinese patent medicines. The oral Chinese patent medicines for treating dysmenorrhea in three major drug lists, guidelines, and textbooks were screened, and the relevant clinical trials were retrieved from eight Chinese and English databases. The key information of the included trials was extracted and visually analyzed. A total of 50 Chinese patent medicines were included, among which oral Chinese patent medicines for the dysmenorrhea patients with the syndrome of Qi stagnation and blood stasis accounted for the highest proportion, and the average daily cost varied greatly among Chinese patent medicines. A total of 150 articles were included, involving 22 Chinese patent medicines, among which Guizhi Fuling Capsules/Pills, Sanjie Zhentong Capsules, and Dan'e Fukang Soft Extract were the most frequently studied. These articles mainly reported randomized controlled trial(RCT), which mainly focused on the comparison of the intervention effect between Chinese patent medicines combined with western medicine and western medicine alone, and the sample size was generally 51-100 cases. The high-frequency outcome indicators belonged to nine domains such as effective rate, adverse reactions, and laboratory examinations. This study showed that oral Chinese patent medicines had advantages in the treatment of dysmenorrhea, and the annual number of related clinical trials showed an overall growing trend. However, there were still problems such as insufficient safety information and vague description of traditional Chinese medicine(TCM) syndromes types in the instructions of Chinese patent medicines. The available clinical research had shortcomings such as uneven distribution of Chinese patent medicines, limited research scale, poor methodological rigor, and insufficient standardization of outcome indicators. In the future, it is necessary to deepen the development of high-quality clinical research and improve the contents of the instructions to ensure the effectiveness and safety of the clinical application of oral Chinese patent medicines in the treatment of dysmenorrhea.
Dysmenorrhea/drug therapy* ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Female ; Administration, Oral ; Nonprescription Drugs/administration & dosage*