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

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Objective To explore the efficacy differences between 3 Tesla magnetic resonance imaging with zero echo time(3T MRI ZTE)and high resolution computed tomography(HRCT)in the detection of pulmonary nodules and the classification diagnosis of the lung imaging reporting and data system(Lung-RADS)in sub-health populations.Methods Clinical and imaging data of 93 patients with pulmonary nodules(126 nodules in total)admitted to some hospital from July to December 2023 were retrospectively analyzed.The 126 nodules were categorized into a benign nodule group(n=51)and a malignant nodule group(n=75)using pathological findings as the gold standard.All the patients underwent examinations by 3T MRI ZTE and HRCT to compare the detection rates of the two measures for pulmonary nodules;the missed and misdiagnosis rates of 3T MRI ZTE,HRCT and Lung-RADS grading were contrasted with the postoperative pathological diagnosis results as the gold standard;comparison analyses of 3T MRI ZTE signs and HRCT signs were performed between the two groups and the patients with different Lung-RADS grades;3T MRI ZTE,HRCT and Lung-RADS grading were compared with the receiver operating characteristic(ROC)curve in terms of diagnosis efficacy for pulmonary nodules,and the consistency analysis was carried out.Results No discernible statistical variation was observed in the detection rates of pulmonary nodules between 3T MRI ZTE and HRCT(P>0.05).Lung-RADS grading had the highest rates of missed diagnosis and misdiagnosis,and 3T MRI ZTE and HRCT had similar detection rates.The malignant nodule group was different from the benign nodule group in the 3T MRI ZTE and HRCT signs in terms of lesion size,spiculation sign,lobulation sign,calcifica-tion,pleural indentation sign,cavity sign,boundary and bronchial cut-off sign,with the differences being statistically signi-ficant(all P<0.05).For the patients of Lung-RADS grade 3,the 3T MRI ZTE and HRCT signs had significant differences in terms of lesion size,spiculation sign,lobulation sign,calcification,pleural indentation sign,cavity sign and bronchial cut-off sign(all P<0.05).For the patients of Lung-RADS grade 4A,the 3T MRI ZTE and HRCT signs had significant differen-ces in terms of lesion size,calcification,boundary and bronchial cut-off sign(all P<0.05).For the patients of Lung-RADS grade 4B,the 3T MRI ZTE and HRCT signs had significant differences in terms of lesion size and calcification(all P<0.05).For the patients of Lung-RADS grade 4X,there were no significant differences found between the 3T MRI ZTE and HRCT signs(all P>0.05).HRCT had the highest sensitivity,specificity,accuracy,AUC value,predictive values and Kappa value for benign and malignant nodules,3T MRI ZTE had the values slightly lower than those of HRCT,and Lung-RADS grading had the lowest values when compared with HRCT and 3T MRI ZTE.Conclusion HRCT and 3T MRI ZTE are complementary for the evaluation of pulmonary nodules,and the differences in imaging signs between them show graded dependence.3T MRI ZTE and HRCT have no significant differences in the detection rate of pulmonary nodules,while HRCT gains advanta-ges in differentiating benign and malignant pulmonary nodules,and references are provided for the screening and clinical early diagnosis of pulmonary nodules.[Chinese Medical Equipment Journal,2025,46(9):52-59]

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.

OBJECTIVE: To investigate the regulatory effect of electroacupuncture (EA) at "Neiguan" (PC6) on mitochondrial autophagy in rats with myocardial ischemia-reperfusion injury (MIRI) at different phases (ischemia and reperfusion phases), and to explore the bidirectional regulatory effects of EA at "Neiguan" (PC6) and its potential mechanism. METHODS: Forty-five male SD rats were randomly divided into 6 groups according to the random number table method, namely, sham-operation group (n=9), model-A group (n=6), model-B group (n=9), EA-A1 group (n=6), EA-B1 group (n=6), and EA-B2 group (n=9). Except the rats in the sham-operation group, the MIRI model was established in the other groups with the physical ligation and tube pushing method. In the model-A group, the samples were collected directly after ligation, and in the model-B group, the samples were collected after ligation and reperfusion. In the EA-A1 group, EA was delivered while the ligation was performed, and afterwards, the samples were collected. In the EA-B1 group, while the ligation was performed, EA was operated at the same time, and after reperfusion, the samples were collected. In the EA-B2 group, during ligation and the opening of the left anterior descending branch of the coronary artery, EA was delivered, and after reperfusion, the samples were collected. EA was performed at bilateral "Neiguan" (PC6), with a disperse-dense wave, a frequency of 2 Hz/100 Hz, a current of 1 mA, and a duration of 30 min. HE staining was employed to observe the morphology of cardiomyocytes, TUNEL was adopted to detect the apoptosis of cardiomyocytes, transcriptome sequencing was to detect the differentially expressed genes in the left ventricle, JC-1 flow cytometry was to detect the mitochondrial membrane potential (MMP) of cardiomyocytes, Western blot was to detect the protein expression of phosphatase and tensin homolog-induced kinase 1 (Pink1), Parkin and p62 in the left ventricle of rats, and ELISA was to detect the levels of serum creatine kinase isoenzyme (CK-MB) and cardiac troponin I (cTn-I) in the rats. RESULTS: Compared with the sham-operation group, the cardiomyocytes of rats in the model-B group were severely damaged, with disordered arrangement, unclear boundaries, broken muscle fibers, edema and loose distribution; and the cardiomyocytes in the EA-B2 group were slightly damaged, the cell structure was partially unclear, the cells were arranged more regularly, and the intact cardiomyocytes were visible. Compared with the sham-operation group, the apoptosis of cardiomyocytes increased in the model-B group (P<0.001); and when compared with the model-B group, the apoptosis alleviated in the EA-B2 group (P<0.001). The differentially expressed genes among the EA-B2 group, the sham-operation group and the model-B group were closely related to cell autophagy and mitochondrial autophagy. Compared with the sham-operation group, MMP of cardiomyocytes was reduced (P<0.001), the protein expression of Pink1, Parkin, and p62 of the left ventricle and the levels of serum CK-MB and cTn-I were elevated in the model B group (P<0.001). In comparison with model-A group, the MMP of cardiomyocytes and the levels of serum CK-MB and cTn-I were reduced (P<0.001, P<0.05), and the protein expression of Pink1 in the left ventricle rose in the EA-A1 group (P<0.01). Compared with the model-B group, MMP of cardiomyocytes increased (P<0.001), the protein expression of Pink1, Parkin, and p62 of the left ventricle, and the levels of serum CK-MB and cTn-I decreased (P<0.001) in the EA-B1 group and the EA-B2 group. When compared with the EA-A1 group, MMP of cardiomyocytes increased (P<0.001), and the protein expression of Pink1, Parkin, and p62 of the left ventricle, and the levels of serum CK-MB and cTn-I decreased in the EA-B1 group (P<0.01). CONCLUSION EA at "Neiguan" (PC6) can ameliorate MIRI in rats, which may be achieved through the Pink1/Parkin-mediated mitochondrial autophagy pathway. EA can alleviate myocardial injury by enhancing mitochondrial autophagy at the ischemia phase, and it can reduce reperfusion injury by weakening mitochondrial autophagy at the reperfusion phase.
Animals ; Electroacupuncture ; Male ; Myocardial Reperfusion Injury/metabolism* ; Rats, Sprague-Dawley ; Rats ; Acupuncture Points ; Autophagy ; Humans ; Mitochondria/genetics*

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.