Diabetic kidney disease（DKD） is a chronic kidney structural and functional disorder caused by diabetes. With the global prevalence of diabetes continuing to rise， DKD has gradually become a major cause of chronic kidney disease and end-stage renal disease（ESRD）， posing a serious threat to patients' quality of life and long-term health outcomes. Studies have shown that apoptosis plays a pivotal role in the development and progression of DKD， with its mechanisms involving abnormal activation of multiple signaling pathways such as Toll-like receptor 4（TLR4）/nuclear transcription factor-κB（NF-κB）/B-cell lymphoma-2（Bcl-2）/cysteinyl aspartate-specific proteinase（Caspase）-3， protein kinase R-like endoplasmic reticulum kinase（PERK）/eukaryotic initiation factor 2α（eIF2α）/activating transcript factor 4（ATF4）/CCAAT enhancer-binding protein homologous protein（CHOP）， phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt）/glycogen synthase kinase-3β（GSK-3β）， Janus kinase 2（JAK2）/signal transducer and activator of transcription 3（STAT3）， adenosine monophosphate-activated protein kinase（AMPK）/mammalian target of rapamycin（mTOR） and silent information regulator 1（SIRT1）/tumor suppressor protein 53（p53）， thereby accelerating renal pathological damage in DKD. Extensive evidence-based medical studies have confirmed that traditional Chinese medicine（TCM）， leveraging its unique therapeutic advantages of multi-target， multi-component and multi-pathway approaches， has demonstrated remarkable efficacy and favorable safety profiles in treating DKD. Recent studies have demonstrated that active components of TCM can specifically target and modulate key effectors in apoptotic signaling pathways. Meanwhile， traditional compound formulations exert synergistic effects through multiple approaches such as replenishing deficiency and activating blood circulation， detoxifying and dredging collaterals， tonifying kidney essence， and removing stasis and purging turbidity， thereby comprehensively regulating critical pathological processes including endoplasmic reticulum stress and mitochondrial apoptosis pathways. This combined therapeutic approach of molecular targeting and holistic regulation provides novel strategies for delaying the progression of DKD. Based on this， this paper provides an in-depth analysis of key apoptotic signaling pathways and their regulatory mechanisms， while systematically summarizing recent research advances regarding the therapeutic effects of TCM active components， compound formulations， and proprietary Chinese medicines on DKD through modulation of these pathways， with particular emphasis on their underlying molecular mechanisms. These findings not only elucidate the modern scientific connotation and theoretical basis of TCM in treating DKD but also establish a solid theoretical and practical foundation for promoting the wider clinical application and further research of TCM in the field of DKD treatment.

Background In 2021, chronic obstructive pulmonary disease (COPD) emerged as the forth leading cause of death in the world. However, the impact of air pollutants on COPD is still inconsistent across current studies. Objective To analyze the relationship between ambient sulfur dioxide (SO₂) exposure and hospital admissions for COPD in Lanzhou, and to examine the modified effects of SO₂ across different genders, age groups, and seasons. Methods A total of 20718 hospital admission records for COPD were collected from Lanzhou between 2016 and 2020, alongside synchronized data on SO₂ concentration and meteorological variables. A generalized additive model integrated with a distributed lag nonlinear model was used to assess the relationship and the lag effects between SO₂ exposure and COPD admissions. Potential confounders, including meteorological factors, day-of-the-week effect, and holidays, were controlled for, with a maximum lag period set at 7 d. Two-pollutant models and sensitivity analyses were conducted to ensure robustness. Results are expressed as relative risks (RR) with 95% confidence intervals (95%CI). Results During the study period, the average daily number of COPD admissions was 11.34±7.03. The average mass concentration of SO₂ was (23.72±12.35) μg·m⁻³. SO₂ exposure was positively correlated with COPD admissions; specifically, each 10 μg·m⁻³ increase in SO₂ concentration was associated with an RR of 1.440 (95%CI: 1.317, 1.573). Stratified analyses found that at a cumulative lag of 7 d, the RRs were higher among females, individuals aged ≥65 years, and during the cold season. Conversely, no significant increase in COPD admission risk related to SO₂ was observed during the warm season. For every 10 μg·m⁻³ increase in SO₂ concentration, the RR was 1.483 (95%CI: 1.311, 1.678) for females, 1.441 (95%CI: 1.311, 1.583) for those aged > 65 years， and 1.595 (95%CI: 1.313, 1.937) during the cold season. Conclusion Short-term exposure to ambient SO₂ exposure in Lanzhou is associated with an increased risk of COPD admission. The association is more pronounced among females, the elderly (aged> 65 years) and during the cold season.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveTo explore the molecular mechanism of aerobic exercise to improve hippocampal neuronal degeneration by regulating tryptophan metabolic pathway. Methods60 SPF-grade C57BL/6J male mice were divided into a young group (2 months old, n=30) and a senile group (12 months old, n=30), and each group was further divided into a control group (C/A group, n=15) and an exercise group (CE/AE group, n=15). An aerobic exercise program was used for 8 weeks. Learning memory ability was assessed by Y-maze, and anxiety-depression-like behavior was detected by absent field experiment. Hippocampal Trp levels were measured by GC-MS. Nissl staining was used to observe the number and morphology of hippocampal neurons, and electron microscopy was used to detect synaptic ultrastructure. ELISA was used to detect the levels of hippocampal Trp,5-HT, Kyn, KATs, KYNA, KMO, and QUIN; Western blot was used to analyze the activities of TPH2, IDO1, and TDO enzymes. ResultsGroup A mice showed significant decrease in learning and memory ability (P<0.05) and increase in anxiety and depressive behaviors (P<0.05); all of AE group showed significant improvement (P<0.05). Hippocampal Trp levels decreased in group A (P<0.05) and increased in AE group (P<0.05). Nidus vesicles were reduced and synaptic structures were degraded in group A (P<0.05), and both were significantly improved in group AE (P<0.05). The levels of Trp, 5-HT, KATs, and KYNA were decreased (P<0.05) and the levels of Kyn, KMO, and QUIN were increased (P<0.05) in group A. The activity of TPH2 was decreased (P<0.05), and the activities of IDO1 and TDO were increased (P<0.05). The AE group showed the opposite trend. ConclusionThe aging process significantly reduces the learning memory ability and increases the anxiety-depression-like behavior of mice, and leads to the reduction of the number of nidus vesicles and degenerative changes of synaptic structure in the hippocampus, whereas aerobic exercise not only effectively enhances the spatial learning memory ability and alleviates the anxiety-depression-like behavior of aging mice, but also improves the morphology and structure of neurons in hippocampal area, which may be achieved by the mechanism of regulating the tryptophan metabolic pathway.

Objective To investigate the independent risk factors affecting the prognosis of chronic active antibody-mediated rejection (caAMR) after kidney transplantation. Methods A retrospective analysis was conducted on 61 patients who underwent renal biopsy and were diagnosed with caAMR. The patients were divided into caAMR group (n=41) and caAMR+TCMR group (n=20) based on the presence or absence of concurrent acute T cell-mediated rejection (TCMR). The patients were followed up for 3 years. The value of 24-hour urinary protein and estimated glomerular filtration rate (eGFR) at the time of biopsy in predicting graft loss was assessed using receiver operating characteristic (ROC) curves. The independent risk factors affecting caAMR prognosis were analyzed using the LASSO-Cox regression model. The correlation between grouping, outcomes, and Banff scores was compared using Spearman rank correlation matrix analysis. Kaplan-Meier analysis was used to evaluate the renal allograft survival rates of each subgroup. Results The 3-year renal allograft survival rates for the caAMR group and the caAMR+TCMR group were 83% and 79%, respectively. The area under the ROC curve (AUC) for predicting 3-year renal allograft loss was 0.83 ［95% confidence interval (CI) 0.70-0.97］ for eGFR and 0.78 (95% CI 0.61-0.96) for 24-hour urinary protein at the time of biopsy. LASSO-Cox regression analysis and Kaplan-Meier analysis showed that eGFR≤25.23 mL/(min·1.73 m²) and the presence of donor-specific antibody (DSA) against human leukocyte antigen (HLA) class I might be independent risk factors affecting renal allograft prognosis, with hazard ratios of 7.67 (95% CI 2.18-27.02) and 5.13 (95% CI 1.33-19.80), respectively. A strong correlation was found between the Banff chronic lesion indicators of renal interstitial fibrosis and tubular atrophy (P<0.05). Conclusions The presence of HLA class I DSA and eGFR≤25.23 mL/(min·1.73 m²) at the time of biopsy may be independent risk factors affecting the prognosis of caAMR.

ObjectiveTo investigate the anti-inflammatory effect and mechanism of Danggui Shaoyaosan （DSS） in ameliorating vascular dementia （VaD）. MethodsSeventy 8-week-old C57BL/6J male mice were randomized into 7 groups： control， model， sham， positive control （donepezil， 10 mg·kg^-1）， and low-， medium-， and high-dose （12， 24， 36 g·kg^-1， respectively） DSS groups （n=10）. After drug administration， behavioral tests and cerebral blood flow detection were carried out. Enzyme-linked immunosorbent assay was used to assess the levels of interferon （IFN）-α， tumor necrosis factor （TNF）-α， interleukin （IL）-6， and CXC motif chemokine ligand 10 （CXCL10） in the brain tissue. Hematoxylin-eosin staining was employed to observe the changes in hippocampal morphology. Transcriptomics was used to predict the potential signaling mechanisms of DSS in ameliorating neuroinflammation， and Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） were conducted to verify the expression changes of related genes. ResultsCompared with the control group and the sham group， the model group showed deceases in recognition index in the new object recognition test， movement distance and time in the target quadrant， and number of crossings in the Morris water maze test （P<0.01）. In addition， the model group showed slow cerebral blood flow and down-regulated protein levels of postsynaptic density protein 95 （PSD95） and occludin （P<0.05， P<0.01）， and there was no significant difference between the control group and the sham group. Compared with the model group， the DSS at each dose increased the new object recognition index， the distance and time in the target quadrant， the number of crossings， and cerebral blood flow （P<0.05， P<0.01）. In terms of brain tissue injury-related protein expression and inflammatory factor content， the medium-dose DSS group had the best effect， with higher protein levels of PSD95 and occludin （P<0.05） and lower levels of IFN-α， TNF-α， and IL-6 （P<0.01） than the model group. The hippocampus of model mice showed pathological manifestations such as cell loss and disarrangement， which were alleviated after administration of DSS at each dose. Transcriptomic results indicated that interferon regulatory factor 7 （IRF7）， signal transducer and activator of transcription 3 （STAT3）， and bone marrow stromal cell antigen 2 （BST2） were differentially expressed genes （down-regulated） in control group and medium-dose DSS group compared with the model group. The KEGG pathway enrichment analysis showed that RIG-Ⅰ-like receptor signaling pathway， Toll-like receptor signaling pathway， cytosolic DNA-sensing pathway， and downstream stimulator of interferon genes （STING） were both upstream signaling pathways affecting IFN expression. Real-time PCR results indicated that the mRNA levels of cyclic good manufacturing practice（GMP）-adenosine monophosphate（AMP） synthase （cGAS）， STING， IRF7， STAT3， and BST2 in the model group were higher than those in the sham group （P<0.05， P<0.01）. The mRNA levels of all the genes in the medium-dose DSS group were down-regulated compared with those in the model group after administration （P<0.05， P<0.01）. Western blot results indicated that the relative expression levels of cGAS， STING， p-IRF7， p-STAT3， and BST2 in the model group were higher than those in the sham group （P<0.05， P<0.01）. The protein levels of cGAS， STING， p-IRF7， p-STAT3， and BST2 in the medium-dose DSS group were decreased compared with those in the model group （P<0.05， P<0.01）. ConclusionDSS ameliorates cognitive impairment in the VaD model mice by inhibiting the cGAS-STING/IRF7/STAT3-mediated neuroinflammation.

ObjectiveTo investigate the anti-inflammatory effect and mechanism of Danggui Shaoyaosan （DSS） in ameliorating vascular dementia （VaD）. MethodsSeventy 8-week-old C57BL/6J male mice were randomized into 7 groups： control， model， sham， positive control （donepezil， 10 mg·kg^-1）， and low-， medium-， and high-dose （12， 24， 36 g·kg^-1， respectively） DSS groups （n=10）. After drug administration， behavioral tests and cerebral blood flow detection were carried out. Enzyme-linked immunosorbent assay was used to assess the levels of interferon （IFN）-α， tumor necrosis factor （TNF）-α， interleukin （IL）-6， and CXC motif chemokine ligand 10 （CXCL10） in the brain tissue. Hematoxylin-eosin staining was employed to observe the changes in hippocampal morphology. Transcriptomics was used to predict the potential signaling mechanisms of DSS in ameliorating neuroinflammation， and Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） were conducted to verify the expression changes of related genes. ResultsCompared with the control group and the sham group， the model group showed deceases in recognition index in the new object recognition test， movement distance and time in the target quadrant， and number of crossings in the Morris water maze test （P<0.01）. In addition， the model group showed slow cerebral blood flow and down-regulated protein levels of postsynaptic density protein 95 （PSD95） and occludin （P<0.05， P<0.01）， and there was no significant difference between the control group and the sham group. Compared with the model group， the DSS at each dose increased the new object recognition index， the distance and time in the target quadrant， the number of crossings， and cerebral blood flow （P<0.05， P<0.01）. In terms of brain tissue injury-related protein expression and inflammatory factor content， the medium-dose DSS group had the best effect， with higher protein levels of PSD95 and occludin （P<0.05） and lower levels of IFN-α， TNF-α， and IL-6 （P<0.01） than the model group. The hippocampus of model mice showed pathological manifestations such as cell loss and disarrangement， which were alleviated after administration of DSS at each dose. Transcriptomic results indicated that interferon regulatory factor 7 （IRF7）， signal transducer and activator of transcription 3 （STAT3）， and bone marrow stromal cell antigen 2 （BST2） were differentially expressed genes （down-regulated） in control group and medium-dose DSS group compared with the model group. The KEGG pathway enrichment analysis showed that RIG-Ⅰ-like receptor signaling pathway， Toll-like receptor signaling pathway， cytosolic DNA-sensing pathway， and downstream stimulator of interferon genes （STING） were both upstream signaling pathways affecting IFN expression. Real-time PCR results indicated that the mRNA levels of cyclic good manufacturing practice（GMP）-adenosine monophosphate（AMP） synthase （cGAS）， STING， IRF7， STAT3， and BST2 in the model group were higher than those in the sham group （P<0.05， P<0.01）. The mRNA levels of all the genes in the medium-dose DSS group were down-regulated compared with those in the model group after administration （P<0.05， P<0.01）. Western blot results indicated that the relative expression levels of cGAS， STING， p-IRF7， p-STAT3， and BST2 in the model group were higher than those in the sham group （P<0.05， P<0.01）. The protein levels of cGAS， STING， p-IRF7， p-STAT3， and BST2 in the medium-dose DSS group were decreased compared with those in the model group （P<0.05， P<0.01）. ConclusionDSS ameliorates cognitive impairment in the VaD model mice by inhibiting the cGAS-STING/IRF7/STAT3-mediated neuroinflammation.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.