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.

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

Infertility, defined as the inability to conceive after 1 year of regular unprotected intercourse, impacts 10%-20% of couples globally. Both male and female factors contribute equally to this condition. Azoospermia, particularly nonobstructive azoospermia (NOA), which affects 10%-15% of infertile men, represents a significant challenge in male infertility. The advent of assisted reproductive technology (ART), specifically microdissection testicular sperm extraction (micro-TESE) followed by intracytoplasmic sperm injection (ICSI), offers a possibility for men with NOA to father biological children. Recent studies have focused on the predictors of sperm retrieval in NOA patients, such as age, testicular volume, and follicle-stimulating hormone (FSH) level. This review aims to explore the limited data on the anatomical characteristics of NOA patients and provide surgical considerations for micro-TESE, thereby enhancing understanding and improving outcomes for this challenging condition.
Humans ; Azoospermia/surgery* ; Male ; Testis/pathology* ; Sperm Retrieval ; Scrotum/pathology* ; Sperm Injections, Intracytoplasmic ; Microdissection

Oncological microdissection testicular sperm extraction (onco-micro-TESE) represents a significant breakthrough for patients with nonobstructive azoospermia (NOA) and a concomitant in situ testicular tumor, to be managed at the time of sperm retrieval. Onco-micro-TESE addresses the dual objectives of treating both infertility and the testicular tumor simultaneously. The technique is intricate, necessitating a comprehensive understanding of testicular anatomy, physiology, tumor biology, and advanced microsurgical methods. It aims to carefully extract viable spermatozoa while minimizing the risk of tumor dissemination. This review encapsulates the procedural intricacies, evaluates success determinants, including tumor pathology and spermatogenic tissue health, and discusses the implementation of imaging techniques for enhanced surgical precision. Ethical considerations are paramount, as the procedure implicates complex decision-making that weighs the potential oncological risks against the profound desire for fatherhood using the male gametes. The review aims to provide a holistic overview of onco-micro-TESE, detailing methodological advances, clinical outcomes, and the ethical landscape, thus offering an indispensable resource for clinicians navigating this multifaceted clinical scenario.
Humans ; Male ; Azoospermia/therapy* ; Testicular Neoplasms/pathology* ; Sperm Retrieval ; Microdissection/methods* ; Testis/surgery*

Surgical methods for varicocele remain controversial. This study intends to evaluate the efficacy and safety of different surgical approaches for treating varicocele through a network meta-analysis (NMA). PubMed, Embase, Cochrane, and Web of Science databases were thoroughly searched. In total, 13 randomized controlled trials (RCTs) and 24 cohort studies were included, covering 9 different surgical methods. Pairwise meta-analysis and NMA were performed by means of random-effects models, and interventions were ranked based on the surface under the cumulative ranking curve (SUCRA). According to the SUCRA, microsurgical subinguinal varicocelectomy (MSV; 91.6%), microsurgical retroperitoneal varicocelectomy (MRV; 78.2%), and microsurgical inguinal varicocelectomy (MIV; 76.7%) demonstrated the highest effectiveness in reducing postoperative recurrence rates. In this study, sclerotherapy embolization (SE; 87.2%), MSV (77.9%), and MIV (67.7%) showed the best results in lowering the risk of hydrocele occurrence. MIV (82.9%), MSV (75.9%), and coil embolization (CE; 58.7%) were notably effective in increasing sperm motility. Moreover, CE (76.7%), subinguinal approach varicocelectomy (SV; 69.2%), and SE (55.7%) were the most effective in increasing sperm count. SE (82.5%), transabdominal laparoscopic varicocelectomy (TLV; 76.5%), and MRV (52.7%) were superior in shortening the length of hospital stay. The incidence rates of adverse events for MRV (0), SE (3.3%), and MIV (4.1%) were notably low. Cluster analyses indicated that MSV was the most effective in the treatment of varicocele. Based on the existing evidence, MSV may represent the optimal choice for varicocele surgery. However, selecting clinical surgical strategies requires consideration of various factors, including patient needs, surgeon experience, and the learning curve.
Humans ; Male ; Embolization, Therapeutic/methods* ; Microsurgery/methods* ; Randomized Controlled Trials as Topic ; Sclerotherapy/methods* ; Treatment Outcome ; Urologic Surgical Procedures, Male/methods* ; Varicocele/surgery*

OBJECTIVES: To analyze the multimorbidity patterns and core diseases among hospitalized patients in different age groups and to explore the impacts of multimorbidity patterns on hospitalization costs. METHODS: Electronic medical records of adult inpatients (aged ≥18 years) from Ningbo Medical Center Lihuili Hospital between January 1, 2018, and June 30, 2023 were collected. The multimorbidity status involving 53 specific diseases was analyzed across different age groups. Association rule mining was used to identify common multimorbidity patterns. Complex network analysis was used to identify core diseases within the multimorbidity networks. Generalized estimating equations (GEE) were used to analyze the impact of different multimorbidity patterns on hospitalization costs. RESULTS: The prevalence of multimorbidity among the 359 402 adult inpatients was 38.51%, with higher rates observed in males (43.60%) and elderly patients (58.29%). Association rule mining identified 15 common multimorbidity patterns, which exhibited differences across age groups. The most prevalent multimorbidity pattern overall was "diabetes→hypertension" (support=7.04%, confidence=62.17%, lift=2.17). In the young adult group, the most prevalent pattern was "dyslipidemia→chronic liver disease" (support=1.19%, confidence=53.17%, lift=6.04). In the middle-aged group, it was "diabetes→hypertension" (support=4.84%, confidence=50.28%, lift=2.15). In the elderly group, it was "coronary heart disease, diabetes→hypertension" (support=2.38%, confidence=77.43%, lift=1.63). Complex network analysis revealed that the core diseases within multimorbidity networks differed across age groups. The core disease identified in the young adult group was chronic liver disease (degree centrality=50, betweenness centrality=0.055, closeness centrality=0.963). Core diseases in the middle-aged group included hypertension, chronic liver disease, and diabetes (all with degree centrality=52, betweenness centrality=0.022, closeness centrality=1.000). Core diseases in the elderly group comprised hypertension, diabetes, malignant tumors, chronic liver disease, thyroid disease, anemia, and arrhythmia (all with degree centrality=52, betweenness centrality=0.009, closeness centrality=1.000). Generalized estimating equations analysis indicated that, most multimorbidity patterns were significantly associated with increased hospitalization costs. However, the magnitude of cost increase varied across different multimorbidity patterns. Specifically, hospitalization costs for patients with patterns such as "heart failure→hypertension", "stroke→hypertension", "malignant tumor, diabetes→hypertension", "stroke, diabetes→hypertension", and "diabetes, heart failure→hypertension" were more than double those of patients without any target diseases. CONCLUSIONS Multimorbidity patterns and core diseases among hospitalized patients differ significantly across age groups, and different patterns exert varying impacts on hospitalization costs. These findings underscore the necessity for age-stratified and multimorbidity pattern specific management strategies.
Humans ; Multimorbidity ; Male ; Hospitalization/economics* ; Female ; Aged ; Middle Aged ; Adult ; Age Factors ; Young Adult ; Adolescent ; Diabetes Mellitus/epidemiology* ; Electronic Health Records ; Aged, 80 and over ; Hospital Costs ; China/epidemiology* ; Hypertension/economics* ; Liver Diseases/epidemiology*

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.

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.