Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

AIM To investigate the effects of Wenyang Jiedu Tongluo Recipe(WYJDTLR)on macrophage recruitment and polarization function in a mouse model of diabetic kidney disease(DKD).METHODS 50 db/db mice were randomly divided into the model group,the valsartan group(10.29 mg/kg)and the high-dose,medium-dose and low-dose WYJDTLR groups(26.52,13.26 and 6.63 g/kg),with 10 mice in each group,in contrast to another 10 db/m mice of the blank group.After 8 weeks of administration,the mice had their levels of fasting blood glucose,24-hour urinary protein quantity(24h-UTP),serum creatinine(Scr)and blood urea nitrogen(BUN)observed;their morphological changes of renal tissues observed by HE staining;their degree of renal glycogen deposition observed by PAS staining;their degree of renal fibrosis observed by Masson staining;their levels of MCP-1 and MCF-1 in serum and TNF-α and IL-1 β in renal tissue detected by ELISA;their renal protein expressions of VCAM-1 and ICAM-1 detected by IHC and Western blot;and their renal expressions of CD86 and CD206 detected by IF.RESULTS Compared with the model group,the WYJDTLR groups displayed decreased levels of fasting blood glucose,24h-UTP,Scr and BUN(P＜0.05,P＜0.01);improved degree of glomerular hypertrophy,mild proliferation of mesangial cells,dilatation of renal tubular,vacuolar degeneration of renal tubular epithelial cells,deposition of glomerular glycogen,and fibrosis of renal tissues(P＜0.01);decreased levels of MCP-1 and MCF-1 in serum and TNF-α and IL-1β in renal tissue(P＜0.05,P＜0.01);decreased renal protein expressions of VCAM-1 and ICAM-1(P＜0.05,P＜0.01),thus reduced the recruitment of macrophages to the kidney;decreased renal CD86 protein expression(P＜0.01);and increased CD206 protein expression(P＜0.01),thus inhibited M1-type polarization of macrophages and promoted M2-type polarization of macrophages.CONCLUSION WYJDTLR can delay the DKD progression in mice by reducing the occurrence of inflammatory reactions through reducing the level of macrophage recruitment factor,inhibiting the M1-type polarization,and promoting the M2-type polarization.

Objective:To compare the efficacy and safety of a domestic hair follicle extraction system versus traditional follicular unit excision (FUE) in extracting hair follicles for the treatment of androgenetic alopecia (AGA) .Methods:A multicenter, randomized, self-controlled clinical trial was conducted on AGA patients aged 18 - 59 years who were recruited from the Huashan Hospital, Fudan University, the Affiliated Hangzhou First People's Hospital, and the China-Japan Friendship Hospital between June 2023 and September 2024. Each patient's scalp was randomly divided into two sides (experimental side vs. control side) using an envelope method. The experimental side underwent robotic hair transplantation with a domestic hair follicle extraction system, and the control side underwent traditional FUE. Hair follicles were extracted from the safe donor area in the occipital region, and implanted into the ipsilateral hair loss area. The primary outcome was the hair transection rate which was calculated immediately after follicular extraction. The secondary outcomes included the hair follicle unit loss rate and the change in hair density at the recipient site on postoperative day 14. Safety was evaluated by assessing the incidence of folliculitis at the donor site on postoperative day 14 and the overall incidence of adverse events. Surgical outcomes were evaluated at 9 months after surgery. Comparisons of evaluation indicators among groups were performed by using a paired t test or Wilcoxon signed-rank test. Results:A total of 55 patients with AGA (51 males and 4 females, aged 32.71 ± 5.75 years) completed the hair follicle transplantation and postoperative follow-up. The hair transection rate ( M[ Q1, Q3]) was 6.65% (4.56%, 10.16%) in the experimental group and 5.28% (3.04%, 8.89%) in the control group (difference = 1.24%, 95% CI: -0.24%, 2.65%) . The hair follicle unit loss rate was 2.00% (1.00%, 3.50%) in the experimental group and 0.50% (0, 2.00%) in the control group, with a significant difference between the two groups ( P = 0.008) . On postoperative day 14, there was no significant difference in the hair density between the experimental group and control group (72.20 ± 25.95 per cm 2vs. 76.49 ± 30.84 per cm 2, P = 0.173) . At 9-month follow-up, both groups showed improvement in the investigator's overall score in the recipient areas. Seven adverse events occurred in 7 subjects (12.72%) in each group, and all were mild folliculitis. Conclusion:The domestic hair follicle extraction system demonstrated comparable efficacy and safety to the traditional FUE in hair transplantation.

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.

This study selected 6 529 plant-based Chinese materia medica(PCMM) from Chinese Materia Medica as research subjects and applied a random permutation test to explore the overall correlation characteristics between nature and flavor, as well as the correlation characteristics after distinguishing different medicinal parts and harvest seasons. The results showed that the overall correlation characteristics between nature and flavor in PCMM were significantly associated in the following pairs: cold and bitter, cool and bitter, cool and astringent, cool and light, neutral and sweet, neutral and astringent, neutral and light, neutral and sour, hot and pungent, and warm and pungent. When analyzing the data by distinguishing medicinal parts and/or harvest seasons, new correlation patterns emerged, characterized by the disappearance of some significant correlations and the emergence of new ones. When analyzing by medicinal parts alone, significant correlations were found in the following cases: cold and light in leaves, cold and salty in barks, cool and sweet in fruits and seeds, neutral and pungent in whole herbs, neutral and salty in stems, and warm and salty in flowers. However, no significant correlations were found between cool and bitter in stems and other types of herbs, cool and astringent in fruits, seeds, flowers, and other types of herbs, cool and light in leaves, fruits, seeds, barks, flowers and other types of herbs, neutral and sweet in barks, neutral and astringent in whole herbs and stems, neutral and light in leaves, fruits, seeds, and flowers, neutral and sour in whole herbs, stems, barks, flowers, and other types of herbs, and hot and pungent in whole herbs, stems, flowers, and other types of herbs. When analyzing by harvest season alone, significant correlations were found in the following cases: cold and salty, and cool and sour in herbs harvested in winter, and neutral and salty in herbs harvested year-round. However, no significant correlation was found between cool and light in herbs harvested in winter. When considering both medicinal parts and harvest seasons, compared to the independent influence of medicinal parts, 14 new significant correlations emerged(e.g., the correlation between cool and bitter in stems harvested in spring), while 53 previously significant correlations disappeared(e.g., the correlation between cool and bitter in barks harvested in summer). Compared to the independent influence of harvest seasons, 11 new significant correlations appeared(e.g., the correlation between cold and light in barks harvested in autumn), while 50 previously significant correlations disappeared(e.g., the correlation between hot and pungent in leaves harvested in winter). This study is the first to reveal the influence of medicinal parts and harvest seasons on the correlation between nature and flavor in PCMM, which highlights that these two factors can interact and jointly affect nature-flavor correlations. Further research is needed to explore the underlying mechanisms. This study provides a deeper understanding of the inherent scientific connotations of herbal properties and offers a theoretical foundation for the cultivation and harvesting of PCMM.
Seasons ; Plants, Medicinal/growth & development* ; Drugs, Chinese Herbal/chemistry* ; Taste

OBJECTIVE: To evaluate the short-term clinical efficacy of external fixation and internal fixation with steel plate in the treatment of unstable distal radius fractures (AO-23C type), based on the principles of Chinese osteosynthesis (CO). METHODS: Forty-eight patients with unstable distal radius fractures between January 2022 and February 2023 were retrospectively analyzed and divided into the CO external fixation group and internal fixation group. CO external fixation group consisted of 25 patients, including 7 males and 18 females, aged from 37 to 56 years old with an average of ( 52.6±11.3) years old. Among them, there were 7 patients of traffic accidents and 18 patients of falls, resulting in a total of 25 patients of closed fractures and no open fractures, the treatment was conducted using closed reduction and CO external fixation. The internal fixation group consisted of 23 patients, comprising 8 males and 15 females, age ranged from 41 to 59 years old, with an average age of(53.3±13.7) years old. Among them, 8 patients resulted from car accidents while the remaining 15 patients were caused by falls. All 23 patients were closed fractures without any open fractures observed. The technique of open reduction and internal fixation with steel plate was employed. The perioperative data, including injury-operation time, operation duration, blood loss, and length of hospital stay, were assessed in both groups. Additionally, the QuickDASH score and visual analogue scale (VAS) were evaluated. Range of motion and grip strength assessment, imaging findings such as palmar inclination angle, ulnar declination angle, radius length, articular surface step, intra-articular space measurements were also examined along with any complications. RESULTS: The follow-up duration ranged from 0 to 24 months, with an average duration of (16.0±3.8) months. The CO external fixation exhibited significantly shorter time from injury to operation (2.4±3.3) d vs (7.4±3.7) d, shorter operation duration (56.27±15.23) min vs (74.10±5.26) min, lower blood loss (14.52±6.54) ml vs (32.32±10.03) ml, and reduced hospitalization days (14.04±3.24 )d vs (16.45±3.05) d compared to the internal fixation group (P<0.05). The QuickDASH score at 12 months post-operation was (8.21±1.64) in the CO external fixation group, while no significant difference was observed in the internal fixation group (7.04±3.64), P>0.05. There were no statistically significant differences in VAS between two groups at 6 weeks, as well as 1 and 3 months post-surgery (P>0.05). Additionally, there were no significant disparities observed in terms of range of motion and grip strength between two groups at the 2-year follow-up after the operation (P>0.05). After 12 months of surgery, the CO external fixation group exhibited a significantly smaller palmar inclination angle (17.90±2.18) ° vs (19.87±3.21) °, reduced articular surface step (0.11±0.03) mm vs (0.17±0.02) mm, and shorter radius length (8.16±1.11) mm compared to the internal fixation group (9.59±1.02) mm, P<0.05. The ulnar deviation angle and intra-articular space did not show any significant difference between two groups (P>0.05). The reduced fell within the allowable range between the CO external fixation group (23 out of 25 cases) and the internal fixation group (21 out of 23 cases) was not statistically significant (P=0.29). There was no significant difference in complications between the two groups(P>0.05). CONCLUSION Both the CO external fixation and open reduction with plate internal fixation demonstrate clinical efficacy in managing unstable distal radius fractures. The CO external fixation offers advantages in shorter injury-to-operation times, reduced intraoperative blood loss, and decreased surgical durations, while radial shortening is more effectively controlled by internal fixation.
Humans ; Male ; Female ; Middle Aged ; Radius Fractures/physiopathology* ; Adult ; Bone Plates ; Fracture Fixation, Internal/methods* ; External Fixators ; Retrospective Studies ; Fracture Fixation/methods* ; Wrist Fractures