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.

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.

ObjectiveThis paper proposes a novel real-time bedside pulmonary ventilation monitoring method for the diagnosis of chronic obstructive pulmonary disease (COPD), based on electrical impedance tomography (EIT). Four indicators—center of ventilation (CoV), global inhomogeneity index (GI), regional ventilation delay inhomogeneity (RVDI), and the ratio of forced expiratory volume in one second to forced vital capacity (FEV₁/FVC)—are calculated to enable the spatiotemporal assessment of COPD. MethodsA simulation of the respiratory cycles of COPD patients was first conducted, revealing significant differences in certain indicators compared to healthy individuals. The effectiveness of these indicators was then validated through experiments. A total of 93 subjects underwent multiple pulmonary function tests (PFTs) alongside simultaneous EIT measurements. Ventilation heterogeneity under different breathing patterns—including forced exhalation, forced inhalation, and quiet tidal breathing—was compared. EIT images and related indicators were analyzed to distinguish healthy individuals across different age groups from COPD patients. ResultsSimulation results demonstrated significant differences in CoV, GI, FEV₁/FVC, and RVDI between COPD patients and healthy individuals. Experimental findings indicated that, in terms of spatial heterogeneity, the GI values of COPD patients were significantly higher than those of the other two groups, while no significant differences were observed among healthy individuals. Regarding temporal heterogeneity, COPD patients exhibited significantly higher RVDI values than the other groups during both quiet breathing and forced inhalation. Moreover, during forced exhalation, the distribution of FEV₁/FVC values further highlighted the temporal delay heterogeneity of regional lung function in COPD patients, distinguishing them from healthy individuals of various ages. ConclusionEIT technology effectively reveals the spatiotemporal heterogeneity of regional lung function, which holds great promise for the diagnosis and management of COPD.

Objective To analyze the epidemiological characteristics and spatiotemporal clustering of varicella in Lu'an City from 2005 to 2023, and to provide a scientific basis for optimizing varicella prevention and control strategies. Methods Data on varicella cases were collected through the Chinese Center for Disease Control and Prevention Information System. Descriptive epidemiology, temporal trend analysis, seasonal analysis, spatiotemporal clustering analysis, and spatial autocorrelation analysis were conducted using QGIS, JoinPoint, SaTScan and GeoDa software. Results The average annual reported incidence rate of varicella in Lu'an City from 2005 to 2023 was 34.55/100,000, showing a trend of initial increase followed by a decrease. The peak incidence occurred from October to January of the following year (RR=1.97, LLR=1743.95, P=0.001). Students aged 0 to 19 was the primary affected group. Spatiotemporal scan analysis revealed four types of spatiotemporal clusters, with the cluster in Jin'an District from October 2017 to December 2023 being particularly prominent (RR=2.87，LLR=1734.15，P<0.001). Spatial autocorrelation analysis indicated significant clustering of varicella cases in the main urban area (Moran's I=0.216，Z=4.786，P=0.003). Conclusion The incidence of varicella in Lu'an City exhibits distinct seasonal and spatial clustering, and schools and kindergartens in the main urban area are the key to varicella prevention and control. It is necessary to enhance the monitoring of disease outbreaks during peak periods and in key areas, and to increase the two-dose vaccination rate for varicella in areas with case aggregation and among key populations.

Intestinal fibrosis is a major complication of inflammatory bowel disease (IBD), leading to a high incidence of surgical interventions and significant disability. Despite its clinical relevance, no targeted pharmacological therapies are currently available. This review aims to explore the underlying mechanisms driving intestinal fibrosis and address unresolved scientific questions, offering insights into potential future therapeutic strategies. We conducted a literature review using data from PubMed up to October 2024, focusing on studies related to IBD and fibrosis. Intestinal fibrosis results from a complex network involving stromal cells, immune cells, epithelial cells, and the gut microbiota. Chronic inflammation, driven by factors such as dysbiosis, epithelial injury, and immune activation, leads to the production of cytokines like interleukin (IL)-1β, IL-17, and transforming growth factor (TGF)-β. These mediators activate various stromal cell populations, including fibroblasts, pericytes, and smooth muscle cells. The activated stromal cells secrete excessive extracellular matrix components, thereby promoting fibrosis. Additionally, stromal cells influence the immune microenvironment through cytokine production. Future research would focus on elucidating the temporal and spatial relationships between immune cell-driven inflammation and stromal cell-mediated fibrosis. Additionally, investigations are needed to clarify the differentiation origins of excessive extracellular matrix-producing cells, particularly fibroblast activation protein (FAP) + fibroblasts, in the context of intestinal fibrosis. In conclusion, aberrant stromal cell activation, triggered by upstream immune signals, is a key mechanism underlying intestinal fibrosis. Further investigations into immune-stromal cell interactions and stromal cell activation are essential for the development of therapeutic strategies to prevent, alleviate, and potentially reverse fibrosis.
Humans ; Fibrosis/metabolism* ; Inflammatory Bowel Diseases/pathology* ; Animals ; Transforming Growth Factor beta/metabolism* ; Intestines/pathology*

Through evidence mapping, this paper systematically summarized the research evidence on the use of traditional Chinese medicine(TCM) in treating renal anemia, displaying the distribution of evidence in this field. A systematic search was conducted across databases, including CNKI, Wanfang, VIP, SinoMed, Springner, PubMed, Engineering Village, and Web of Science, targeting studies published up to June 30, 2024. The research evidence was summarized and displayed through a combination of graphs, tables, and text. A total of 264 interventional studies, 37 observational studies, and 7 systematic reviews were included. The annual publication volumes related to TCM treatment in renal anemia showed an overall upward trend, with most studies involving sample sizes between 60 and 120 participants(224 articles, 74.42%). Intervention measures were categorized into 21 types, with oral TCM decoctions being the most common medicine(171 times, 56.81%). The use of self-made prescriptions was the most common TCM intervention method. The intervention duration was mainly between 8 weeks and 3 months(239 articles, 79.40%). The most frequently reported TCM syndrome was spleen and kidney Qi deficiency. The top 2 outcome indicators were the anemia indicators and renal injury/renal function markers. However, several issues were identified in these studies, such as insufficient attention to the sources, social/geographical information, and temporal continuity of research subjects in observational research. Randomized controlled trials mostly had a high risk of bias, mainly due to issues such as randomization bias, blinding bias, and failure to register research protocols. The methodology quality of systematic reviews was generally low, mainly due to inadequate inclusion of literature, failure to specify funding sources, and lack of pre-registrations. While the report quality of systematic review was acceptable, there were significant gaps in the reporting of protocols, registration, and funds. The results show that these issues affect the quality of research and the reliability of findings on TCM in treating renal anemia, underscoring the need to address them to conduct higher-quality research and provide more reliable medical evidence for TCM in treating renal anemia.
Humans ; Anemia/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Kidney Diseases/drug therapy*

OBJECTIVE: To investigate the clinical effect of minimally invasive technique in the treatment of moderate and severe gluteal muscle contracture. METHODS: A retrospective study was conducted on 85 patients (170 sides) with bilateral gluteal muscle contracture admitted from January 2016 to December 2019. All patients were treated with minimally invasive release of tendon knife. There were 32 males and 53 females, ranging in age from 15 to 37 years old, with an average age of (22.3±6.3) years old. Operation time, intraoperative blood loss, incision length, first postoperative ambulation time, complication rate, recurrence rate, and Harris hip score (HHS) were analyzed and evaluated. RESULTS: The average follow-up time was (16.2±4.6) months, ranging from 12 to 30 months. The operation time ranged from 7 to 15 min, with an average of (10.2±3.1) min. Intraoperative blood loss ranged from 2 to 20 ml, with an average of (8.4±2.2) ml. The incision length ranged from 0.6 to 2.0 cm, with an average of (0.8±0.3) cm. The time to postoperative ambulation ranged from 12 to 28 h, with an average of (20.0±3.2) h. All patients achieved primary wound healing without sciatic nerve injury or recurrence. HHS hip function scores ranged from 90 to 98, with an average score of (96.2±1.4). Complications included intraoperative tendon blade tip fracture in two cases (removed under fluoroscopic guidance) and subcutaneous hematoma in three cases-two resolved with compression and one with open evacuation.. Twenty-nine patients exhibited transient swaying gait postoperatively, of which 24 patients returned to normal after 4 weeks and 5 patients returned to normal after 6 weeks. CONCLUSION Minimally invasive tendon blade release is a safe and effective technique for treating gluteal muscle contracture, offering minimal trauma, rapid recovery, and excellent cosmetic and functional outcomes. However, it exhibits a low risk of blade tip fracture and sciatic nerve injury, warranting experienced surgical handling.
Humans ; Male ; Female ; Adult ; Minimally Invasive Surgical Procedures/methods* ; Adolescent ; Retrospective Studies ; Buttocks/surgery* ; Young Adult ; Contracture/surgery* ; Tendons/surgery* ; Muscle, Skeletal/surgery*