Gynecological cancer is a life-threatening malignancy among women. Traditional therapies, including chemotherapy, often face challenges in terms of chemotherapeutic drug solubility and resistance, specificity, tumor site targeting, and toxicity to healthy tissues, leading to shortened efficacy and unfavorable patient outcomes and survival rates in patients with gynecologic malignancies. Recently, nanotechnology-based therapeutic methods such as targeted drug delivery and phototherapies have emerged as an appropriate alternative to overcome issues associated with traditional therapeutic methods. Specifically, nanomaterials and nanomaterial-based methods enhance the delivery of therapeutic/targeting agents to tumor sites and cellular uptakes and improve the tumor-suppressing effect. This review aims to provide an overview and future perspective on the potential impact of nanotechnology-based therapeutic methods for effective therapies for gynecologic cancer.

Gynecological cancer is a life-threatening malignancy among women. Traditional therapies, including chemotherapy, often face challenges in terms of chemotherapeutic drug solubility and resistance, specificity, tumor site targeting, and toxicity to healthy tissues, leading to shortened efficacy and unfavorable patient outcomes and survival rates in patients with gynecologic malignancies. Recently, nanotechnology-based therapeutic methods such as targeted drug delivery and phototherapies have emerged as an appropriate alternative to overcome issues associated with traditional therapeutic methods. Specifically, nanomaterials and nanomaterial-based methods enhance the delivery of therapeutic/targeting agents to tumor sites and cellular uptakes and improve the tumor-suppressing effect. This review aims to provide an overview and future perspective on the potential impact of nanotechnology-based therapeutic methods for effective therapies for gynecologic cancer.

Gynecological cancer is a life-threatening malignancy among women. Traditional therapies, including chemotherapy, often face challenges in terms of chemotherapeutic drug solubility and resistance, specificity, tumor site targeting, and toxicity to healthy tissues, leading to shortened efficacy and unfavorable patient outcomes and survival rates in patients with gynecologic malignancies. Recently, nanotechnology-based therapeutic methods such as targeted drug delivery and phototherapies have emerged as an appropriate alternative to overcome issues associated with traditional therapeutic methods. Specifically, nanomaterials and nanomaterial-based methods enhance the delivery of therapeutic/targeting agents to tumor sites and cellular uptakes and improve the tumor-suppressing effect. This review aims to provide an overview and future perspective on the potential impact of nanotechnology-based therapeutic methods for effective therapies for gynecologic cancer.

Gynecological cancer is a life-threatening malignancy among women. Traditional therapies, including chemotherapy, often face challenges in terms of chemotherapeutic drug solubility and resistance, specificity, tumor site targeting, and toxicity to healthy tissues, leading to shortened efficacy and unfavorable patient outcomes and survival rates in patients with gynecologic malignancies. Recently, nanotechnology-based therapeutic methods such as targeted drug delivery and phototherapies have emerged as an appropriate alternative to overcome issues associated with traditional therapeutic methods. Specifically, nanomaterials and nanomaterial-based methods enhance the delivery of therapeutic/targeting agents to tumor sites and cellular uptakes and improve the tumor-suppressing effect. This review aims to provide an overview and future perspective on the potential impact of nanotechnology-based therapeutic methods for effective therapies for gynecologic cancer.

Gynecological cancer is a life-threatening malignancy among women. Traditional therapies, including chemotherapy, often face challenges in terms of chemotherapeutic drug solubility and resistance, specificity, tumor site targeting, and toxicity to healthy tissues, leading to shortened efficacy and unfavorable patient outcomes and survival rates in patients with gynecologic malignancies. Recently, nanotechnology-based therapeutic methods such as targeted drug delivery and phototherapies have emerged as an appropriate alternative to overcome issues associated with traditional therapeutic methods. Specifically, nanomaterials and nanomaterial-based methods enhance the delivery of therapeutic/targeting agents to tumor sites and cellular uptakes and improve the tumor-suppressing effect. This review aims to provide an overview and future perspective on the potential impact of nanotechnology-based therapeutic methods for effective therapies for gynecologic cancer.

Purpose: Predicting improvements in voiding symptoms following deobstructive surgery for male lower urinary tract symptoms/benign prostatic hyperplasia (LUTS/BPH) is challenging when detrusor contractility is impaired. This study aimed to develop an artificial intelligence model that predicts symptom improvement after holmium laser enucleation of the prostate (HoLEP), focusing on changes in maximum flow rate (MFR) and voiding efficiency (VE) 1-month postsurgery. Methods: We reviewed 1,933 patients who underwent HoLEP at Samsung Medical Center from July 2008 to January 2024. The study employed a deep neural network (DNN) for multiclass classification to predict changes in MFR and VE, each divided into 3 categories. For comparison, additional machine learning (ML) models such as extreme gradient boosting, random forest classification, and support vector machine were utilized. To address class imbalance, we applied the least squares method and multitask learning. Results: A total of 1,142 patients with complete data were included in the study, with 992 allocated for model training and 150 for external validation. In predicting MFR, the DNN achieved a microaverage area under the receiver operating characteristic curve (AUC) of 0.884±0.006, sensitivity of 0.783±0.020, and specificity of 0.891±0.010. For VE prediction, the microaverage AUC was 0.817±0.007, with sensitivity and specificity values of 0.660±0.014 and 0.830±0.007, respectively. These results indicate that the DNN's predictive performance was superior to that of other ML models. Conclusions The DNN model provides detailed and accurate predictions for recovery after HoLEP, providing valuable insights for clinicians managing patients with LUTS/BPH.

Purpose: The aim of this study was to investigate the efficacy of ethanol extracts of Cornus alba (ECA) against benign prostatic hyperplasia (BPH) in vitro and in vivo. Materials and Methods: The prostate stromal cells (WPMY-1) and epithelial cells (RWPE-1) were used to examine the action mechanism of ECA in BPH in vitro. ECA efficacy was evaluated in vivo using a testosterone propionate (TP)-induced BPH rat model. Results: Treatment with ECA inhibited the proliferation of prostate cells by inducing G1-phase cell cycle arrest through the regulation of positive and negative proteins. Treatment of prostate cells with ECA resulted in alterations in the mitogen-activated protein kinases and protein kinase B signaling pathways. The transcriptional binding activity of the NF-κB motif was suppressed in both ECA-treated prostate cells. In addition, treatment with ECA altered the level of BPH-associated axis markers (5α-reductase, fibroblast growth factor-2, androgen receptor, epidermal growth factor, Bcl-2, and Bax) in both cell lines. Finally, the administration of ECA attenuated the enlargement of prostatic tissues in the TP-induced BPH rat model, accompanied by histology, immunoblot, and serum dihydrotestosterone levels. Conclusions These results demonstrated that ECA exerted beneficial effects on BPH both in vitro and in vivo and might provide valuable information in the development of preventive or therapeutic agents for improving BPH.

Purpose: The aim of this study was to investigate the efficacy of ethanol extracts of Cornus alba (ECA) against benign prostatic hyperplasia (BPH) in vitro and in vivo. Materials and Methods: The prostate stromal cells (WPMY-1) and epithelial cells (RWPE-1) were used to examine the action mechanism of ECA in BPH in vitro. ECA efficacy was evaluated in vivo using a testosterone propionate (TP)-induced BPH rat model. Results: Treatment with ECA inhibited the proliferation of prostate cells by inducing G1-phase cell cycle arrest through the regulation of positive and negative proteins. Treatment of prostate cells with ECA resulted in alterations in the mitogen-activated protein kinases and protein kinase B signaling pathways. The transcriptional binding activity of the NF-κB motif was suppressed in both ECA-treated prostate cells. In addition, treatment with ECA altered the level of BPH-associated axis markers (5α-reductase, fibroblast growth factor-2, androgen receptor, epidermal growth factor, Bcl-2, and Bax) in both cell lines. Finally, the administration of ECA attenuated the enlargement of prostatic tissues in the TP-induced BPH rat model, accompanied by histology, immunoblot, and serum dihydrotestosterone levels. Conclusions These results demonstrated that ECA exerted beneficial effects on BPH both in vitro and in vivo and might provide valuable information in the development of preventive or therapeutic agents for improving BPH.

Purpose: The aim of this study was to investigate the efficacy of ethanol extracts of Cornus alba (ECA) against benign prostatic hyperplasia (BPH) in vitro and in vivo. Materials and Methods: The prostate stromal cells (WPMY-1) and epithelial cells (RWPE-1) were used to examine the action mechanism of ECA in BPH in vitro. ECA efficacy was evaluated in vivo using a testosterone propionate (TP)-induced BPH rat model. Results: Treatment with ECA inhibited the proliferation of prostate cells by inducing G1-phase cell cycle arrest through the regulation of positive and negative proteins. Treatment of prostate cells with ECA resulted in alterations in the mitogen-activated protein kinases and protein kinase B signaling pathways. The transcriptional binding activity of the NF-κB motif was suppressed in both ECA-treated prostate cells. In addition, treatment with ECA altered the level of BPH-associated axis markers (5α-reductase, fibroblast growth factor-2, androgen receptor, epidermal growth factor, Bcl-2, and Bax) in both cell lines. Finally, the administration of ECA attenuated the enlargement of prostatic tissues in the TP-induced BPH rat model, accompanied by histology, immunoblot, and serum dihydrotestosterone levels. Conclusions These results demonstrated that ECA exerted beneficial effects on BPH both in vitro and in vivo and might provide valuable information in the development of preventive or therapeutic agents for improving BPH.

Purpose: Predicting improvements in voiding symptoms following deobstructive surgery for male lower urinary tract symptoms/benign prostatic hyperplasia (LUTS/BPH) is challenging when detrusor contractility is impaired. This study aimed to develop an artificial intelligence model that predicts symptom improvement after holmium laser enucleation of the prostate (HoLEP), focusing on changes in maximum flow rate (MFR) and voiding efficiency (VE) 1-month postsurgery. Methods: We reviewed 1,933 patients who underwent HoLEP at Samsung Medical Center from July 2008 to January 2024. The study employed a deep neural network (DNN) for multiclass classification to predict changes in MFR and VE, each divided into 3 categories. For comparison, additional machine learning (ML) models such as extreme gradient boosting, random forest classification, and support vector machine were utilized. To address class imbalance, we applied the least squares method and multitask learning. Results: A total of 1,142 patients with complete data were included in the study, with 992 allocated for model training and 150 for external validation. In predicting MFR, the DNN achieved a microaverage area under the receiver operating characteristic curve (AUC) of 0.884±0.006, sensitivity of 0.783±0.020, and specificity of 0.891±0.010. For VE prediction, the microaverage AUC was 0.817±0.007, with sensitivity and specificity values of 0.660±0.014 and 0.830±0.007, respectively. These results indicate that the DNN's predictive performance was superior to that of other ML models. Conclusions The DNN model provides detailed and accurate predictions for recovery after HoLEP, providing valuable insights for clinicians managing patients with LUTS/BPH.