ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

BACKGROUND: Chronic kidney disease (CKD) is a progressive disorder that leads to significant structural and functional changes in the kidneys, posing a major global health concern and contributing to high mortality rates. METHODS: The urgent need for innovative treatments is evident. Mesenchymal stem cells (MSCs) are well-regarded in regenerative medicine for their ability to repair tissue and modulate immune responses. Emerging research indicates that the therapeutic benefits of MSCs are largely mediated by the secretion of extracellular vesicles (EVs), particularly exosomes (MSC-Exos), which replicate the effects of MSCs by delivering genetic materials and proteins to target cells. RESULTS: MSC-Exos are novel natural carriers for targeted gene or drug delivery, offering biocompatibility, intrinsic targeting capabilities, and bioactive cargo to modulate recipient cells. They represent a groundbreaking platform for precision medicine, enhancing therapeutic efficacy with minimal immunogenicity and off-target effects. Moreover, embedding exosomes within hydrogels has emerged as a promising strategy to maintain their biological activity and enable a controlled release. CONCLUSION This review explores the roles of MSC-Exos in CKD pathophysiology, highlights the renoprotective effects of MSC-Exos for various sources, and provides a comprehensive overview of how hydrogel biomaterials present a promising approach for integrating exosomes to enhance therapeutic outcomes. The use of hydrogels to encapsulate exosomes improves their sustained release and stability in diseased kidney tissues, providing an innovative strategy to enhance precision therapies.

[Objective] To analyse and predict the tendencies of using erythrocyte blood in Changsha based on the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) and ARIMA-LSTM combination model, so as to provide reliable basis for designing a feasible and effective blood inventory management strategy. [Methods] The data of erythrocyte usage from hospitals in Changsha between January 2012 and December 2023 were collected, and ARIMA model, LSTM model and ARIMA-LSTM combination model were established. The actual erythrocyte consumption from January to May 2024 were used to assess and verify the prediction effect of the models. The extrapolation prediction accuracy of the models were tested using two evaluation indicators: mean absolute percentage error (MAPE) and root mean square error (RMSE), and then the prediction performance of the model was compared. [Results] The RMSE of LSTM model, optimal model ARIMA(1,1,1)(1,1,1)12 and ARIMA-LSTM combination model were respectively 5 206.66, 3 096.43 and 2 745.75, and the MAPE were 18.78%,11.54% and 9.76% respectively, which indicated that the ARIMA-LSTM combination model was more accurate than the ARIMA model and LSTM model, and the prediction results was basically consistent with the actual situation. [Conclusion] The ARIMA-LSTM model can better predict the clinical erythrocyte consumption in Changsha in the short term.

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.

Objective:To investigate the efficacy and mechanism of botulinum toxin type A (BTX-A) combined with static progressive stretching (SPS) in the treatment of traumatic knee stiffness in rats.Methods:Forty healthy male SD rats aged 8 weeks and weighing 220-300 g, were randomly divided into blank control group ( n=8) and model groups ( n=28) (including injury group, BTX-A group, SPS group and BTX-A+SPS group, with 7 in each group). Hlidebrand′s method was used to construct a traumatic knee stiffness model, with the following main steps: destruction of the joint capsule, Kirschner wire fixation, joint drilling, and removal of the internal fixation at 4 weeks. The blank control group did not receive any treatment and could move freely in the cage. The injury group moved freely after successful modeling. On the day of internal fixation removal, BTX-A was injected into the joint cavity in group BTX-A, SPS treatment was started in the SPS group, BTX-A was injected into the joint cavity and SPS treatment was started in the BTX-A+SPS group. The treatments lasted 16 days. The range of motion (ROM) and joint stiffness were measured before treatment and at 16 days after treatment. At 16 days after treatment, knee joint tissue was collected and the rats were sacrificed, and the articular capsule fibrous tissue proliferation was observed by HE and Masson staining. The expression levels of phosphorylated (p)-Smad2, Smad2, p-Smad3, Smad3, Smad4, transforming growth factor-β1 (TGF-β1), collagen type I, collagen type III, and α-smooth actin (α-SMA) were determined by Western blot. The ratio of phosphorylated protein to total protein was calculated to reflect the phosphorylation level. Results:(1) ROM: Before treatment, the ROM in the blank control group was significantly higher than that in the other groups ( P<0.05), with no significant difference in ROM among the other groups ( P>0.05). At 16 days after treatment, ROM in the injury group, BTX-A group, SPS group, and BTX-A+SPS group was lower than that in the blank control group ( P<0.05), among which ROM in the BTX-A+SPS group was significantly higher than that in the injury group, BTX-A group, and SPS group ( P<0.05). At 16 days after treatment, there was no significant difference in ROM before and after treatment in the blank control group ( P>0.05), and ROM in the other groups was significantly increased compared with that before treatment ( P<0.01). (2) Joint stiffness: At 16 days after treatment, the joint stiffness levels in the injury group, the BTX-A group, and the SPS group were (0.95±0.24)N·cm/°, (0.86±0.22)N·cm/°, and (0.65±0.09)N·cm/° respectively, which were significantly lower than (0.36±0.03)N·cm/° in the blank control group ( P<0.05). The joint stiffness level of the BTX-A+SPS group was (0.49±0.04)N·cm/°, which was not significantly different from that in the blank control group ( P>0.05), but was significantly lower than those in the injury group, BTX-A group, and SPS group ( P<0.05). (3) Fibrous tissue proliferation: at 16 days after treatment, the joint capsular structure in the blank control group was complete and clear, the fibers were arranged in order, and there was no obvious fibrous tissue proliferation. The pathological changes in the injury group were the most serious, with a large number of synovial fibrous tissue proliferation, significantly increased blood vessels in the tissue, and inflammatory cell infiltration. Compared with the SPS group and BTX-A group, the lesions in BTX-A+SPS group were milder, with only slight increase in the number of synovial cells but no obvious vascular proliferation or lymphocytes, and the overall lesions were the least severe. (4) Protein expression: the ratios of p-Smad2/Smad2 in the injury group, BTX-A group and SPS group were 1.552±0.234, 1.328±0.272 and 1.194±0.277 respectively, which were higher than 0.794±0.082 in the blank control group ( P<0.05). The ratio of p-Smad2/Smad2 in the BTX-A+SPS group was 1.013±0.123, which was not significantly different from those in the blank control group, BTX-A group or SPS group ( P>0.05), but was lower than that in the injury group ( P<0.05). At 16 days after treatment, the p-Smad3/Smad3 ratios in the injury group, BTX-A group, SPS group and BTX-A+SPS group were 2.272±0.309, 1.664±0.285, 1.381±0.276 and 1.003±0.060 respectively, which were higher than 0.515±0.051 in the blank control group ( P<0.05). The p-Smad3/Smad3 ratio in the BTX-A+SPS group was significantly lower than those in the injury group, BTX-A group and SPS group ( P<0.05). At 16 days after treatment, the level of Smad4 in the injury group (1.001±0.015) was higher than 0.294±0.076 in the blank control group ( P<0.05). However, there was no significant difference between the BTX-A group (0.664±0.051), SPS group (0.833±0.045), BTX-A+SPS group (0.467±0.068) or the blank control group ( P>0.05). The level of Smad4 in the BTX-A+SPS group was significantly lower than those in the injury group, BTX-A group and SPS group ( P<0.05). At 16 days after treatment, the level of TGF-β1 in the injury group (1.004±0.407) was higher than 0.269±0.122 in the blank control group ( P<0.05), while there was no significant difference between the BTX-A group (0.564±0.194), SPS group (0.422±0.086) and BTX-A+SPS group (0.347±0.161) and the blank control group ( P>0.05). The level of TGF-β1 in the BTX-A+SPS group was significantly lower than those in the injury group, BTX-A group and SPS group ( P<0.05). At 16 days after treatment, the level of type I collagen in the injury group was 0.999±0.170, higher than 0.299±0.139 in the blank control group ( P<0.05), while there was no significant difference between the BTX-A group (0.542±0.278), SPS group (0.561±0.165), and BTX-A+SPS group (0.537±0.045) and the blank control group ( P>0.05). The level of collagen type I in the BTX-A+SPS group was significantly lower than those in the injury group, BTX-A group, and SPS group ( P<0.05). At 16 days after treatment, the level of type III collagen in the injury group was 1.002±0.126, higher than 0.239±0.106 in the blank control group ( P<0.05), while there was no significant difference between the BTX-A group (0.661±0.062), SPS group (0.595±0.062), and BTX-A+SPS group (0.504±0.269) and the blank control group ( P>0.05). The level of collagen type III in the BTX-A+SPS group was significantly lower than those in the injury group, BTX-A group, and SPS group ( P<0.05). At 16 days after treatment, the level of α-SMA in the injury group was 0.998±0.074, higher than 0.130±0.023 in the blank control group ( P<0.05), while there was no significant difference between the BTX-A group (0.358±0.060), SPS group (0.432±0.230), and BTX-A+SPS group (0.293±0.135) and the blank control group ( P>0.05). The level of α-SMA in the BTX-A+SPS group was significantly lower than those in the injury group, BTX-A group and SPS group ( P<0.05). Conclusions:Compared with single treatment, the combination of BTX-A and SPS demonstrates significantly greater efficacy in the treatment of traumatic knee stiffness in rats. This combined approach not only enhances joint mobility and elasticity but also effectively inhibits joint capsule fibrosis. The underlying mechanism may involve the further suppression of TGF-β1 expression in the joint capsule, leading to reduced phosphorylation levels of Smad2 and Smad3. This, in turn, inhibits the binding of Smad2 and Smad3 to the Smad4 receptor, ultimately downregulating the expression of the downstream proteins of the TGF-β/Smad signaling pathway, such as collagen type I, collagen type III and α-SMA.

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.