Objective To analyze the technical essentials of single-microport assisted micro-uni-port thoracoscopic surgery, and to investigate its surgical efficacy and promotional value. Methods Clinical data of patients who consecutively underwent radical upper lobectomy in the Department of Thoracic Surgery of Xi'an International Medical Center Hospital from March 2023 to June 2024 were retrospectively analyzed. According to the surgical approach, patients were divided into two groups: the single-microport assisted group (underwent upper lobectomy via single-microport assisted micro-uni-port thoracoscopy) and the traditional uniportal group (underwent traditional uniportal thoracoscopic lobectomy). Clinical outcomes were compared between the two groups. Results A total of 62 patients were enrolled. There were 30 patients in the single-microport assisted group, with a mean age of (57.4±10.8) years, and 32 patients in the traditional uniportal thoracoscopic group, with a mean age of (57.6±8.7) years. The baseline data were comparable between the two groups. All patients in both groups successfully underwent minimally invasive surgery without conversion to thoracotomy. The operative time in the single-microport assisted group was significantly shorter than that in the traditional uniportal group [(146.03±30.79) min vs. (171.41±36.41) min, P=0.004]. However, there were no statistically significant differences between the two groups in intraoperative blood loss, number of dissected lymph nodes, duration of chest tube drainage, postoperative pain score, postoperative hospital stay, hospitalization cost, or incidence of postoperative complications (all P>0.05). Conclusion Single-microport assisted micro-uni-port thoracoscopic surgery for upper lobectomy can maximally integrate the advantages of three-port and uni-port VATS while effectively avoiding their disadvantages. It significantly shortens the operative time without increasing postoperative pain or complications, and represents a more minimally invasive, safer, and more convenient surgical approach.

In February 2024, a patient with left lumbar pain for over 1 month was admitted to Heze Municipal Hospital and was diagnosed as retroperitoneal mass by enhanced CT and PET/CT examination.Laparoscopic retroperitoneal mass resection was performed.The postoperative pathological diagnosis was Erdheim-Chester disease.The patient was treated with interferon (IFN) alpha after surgery and was followed up for 5 months with no recurrence or metastasis. Erdheim-Chester disease(ECD) is rare and prone to be misdiagnosed clinically. Biopsy or resection of the tumor should be performed to make a definite diagnosis.

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 therapeutic effect of the short-penis treatment apparatus and wide-band infrared therapy apparatus on penile dysplasia (PDP) in children and establish objective parameters for assessing the severity of PDP. METHODS: This study included 252 children received in the Department of pediatric urology of the First Affiliated Hospital of Xinjiang Medical University from January to December 2023, 102 with PDP (the PDP group) and the other 150 with normal penile development (the control group), those of the former group treated with the short-penis therapeutic apparatus and wide-band infrared therapy apparatus. Before and after 30 days of treatment, we measured the flaccid penile length (FPL), stretched penile length (SPL) and penile diameters (PD) of the children, and defined the penile dysplasia index as the FPL/SPL and FPL/PD ratios. RESULTS: The penile parameters exhibited statistically significant differences between the PDP and control groups, (FPL:［1.97±0.72］cm vs ［3.25±0.51］ cm, P<0.01; SPL:［3.80±0.81］cm vs ［5.21±0.79］cm,P<0.01).The FPL remarkably increased in the PDP group after treatment(［1.97±0.72］cm vs ［2.90±1.20］ cm, P<0.01). Both FPL and SPL were notably shorter in the PDP cases than in the controls, with the cutoff values of 0.57 and 2.09, sensitivities of 80.7% and 95.3%, and specificities of 69.6% and 82.4% for FPL/SPL and FPL/PD, respectively. CONCLUSION The short-penis therapeutic apparatus and wide-band infrared therapy apparatus can promote the growth and development of the penis in children. The ratio of FPL/PD can serve as an objective indicator to effectively describe the severity of penile developmental abnormalities.
Humans ; Male ; Penis/abnormalities* ; Child ; Penile Diseases/therapy* ; Child, Preschool ; Infant

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.

The theory of yin-yang is a foundational concept in traditional Chinese medicine (TCM) and has been refined through millennia of clinical practice and theoretical development. This theory remains central to syndrome differentiation and therapeutic decision-making. With rapid advances in modern biomedicine and information sciences, this review synthesizes recent interdisciplinary progress linking yin-yang concepts to cellular metabolism, redox balance, gene regulation, and immunomodulation. We outline how pathological states historically described as “yin deficiency” or “yang hyperactivity” correspond to alterations in cellular energy conversion, molecular signaling networks, and systemic homeostasis, and we critically evaluate current evidence for mechanistic pathways. Notwithstanding promising correlations, major gaps persist in mechanistic clarity and the establishment of quantitative metrics, limiting the rigorous integration of yin-yang theory into evidence-based frameworks. To address these gaps, we propose a research roadmap that leverages modern biotechnology, mathematical modeling, and artificial intelligence for quantitative multiscale analysis. By integrating molecular, cellular, and systemic datasets, this approach can clarify the biological connotations of yin-yang balance for physiology, disease mechanisms, and clinical outcome assessment.

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.