Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

BACKGROUND/OBJECTIVES: Benign prostatic hyperplasia (BPH) is the most common prostate disease and one of the most common chronic diseases caused by aging in men. On the other hand, there has been no research on BPH using Abeliophyllum distichum Nakai (A.distichum). Therefore, this study investigated the effects of A. distichum on BPH.MATERIALS/METHODS: A. distichum leaves were extracted with distilled water, 70% ethanol, and 95% hexane as solvents. Subsequently, the inhibitory effects of each A. distichum extract on androgen receptor (AR) signaling were evaluated in vitro. The testosterone-induced BPH model was then used to confirm the efficacy of A. distichum leaves in 70% ethanol extract (ADLE). RESULTS: ADLE had the strongest inhibitory effect on AR signaling. A comparison of the activity of ADLE by harvest time showed that the leaves of A. distichum harvested in autumn had a superior inhibitory effect on AR signaling to those harvested at other times. In the BPH rat model, the administration of ADLE reduced the prostate size and prostate epithelial cell thickness significantly and inhibited AR signaling. Subsequently, the administration of ADLE also reduced the expression of growth factors, thereby inactivating the PI3K/AKT pathway. CONCLUSIONS An analysis of the efficacy of ADLE to relieve BPH showed that the ethanol extract grown in autumn exhibited the highest inhibitory ability of the androgen-signaling related factors in vitro. ADLE also inhibited the expression of growth factors by inhibiting the expression of the androgen-signaling related factors in vivo. Overall, ADLE is proposed as a functional food that is effective in preventing BPH.

Atrial fibrillation (AF) is the most common supraventricular arrhythmia, and it corresponds highly with exercise intensity. Here, we induced AF in mice using acetylcholine (ACh)-CaCl2 for 7 days and aimed to determine the appropriate exercise intensity (no, low, moderate, high) to protect against AF by running the mice at different intensities for 4 weeks before the AF induction by ACh-CaCl2 . We examined the AF-induced atrial remodeling using electrocardiogram, patch-clamp, and immunohistochemistry. After the AF induction, heart rate, % increase of heart rate, and heart weight/body weight ratio were significantly higher in all the four AF groups than in the normal control; highest in the high-ex AF and lowest in the low-ex (lower than the no-ex AF), which indicates that low-ex treated the AF. Consistent with these changes, G protein-gated inwardly rectifying K + currents, which were induced by ACh, increased in an exercise intensity-dependent manner and were lower in the low-ex AF than the no-ex AF. The peak level of Ca2+ current (at 0 mV) increased also in an exercise intensity-dependent manner and the inactivation time constants were shorter in all AF groups except for the low-ex AF group, in which the time constant was similar to that of the control. Finally, action potential duration was shorter in all the four AF groups than in the normal control; shortest in the high-ex AF and longest in the low-ex AF. Taken together, we conclude that low-intensity exercise protects the heart from AF, whereas high-intensity exercise might exacerbate AF.

Purpose: This study aimed to evaluate the real-world efficacy of immune checkpoint inhibitors (ICIs), and to identify clinicolaboratory factors to predict treatment outcomes in patients with advanced esophageal squamous cell carcinoma (ESCC) receiving ICIs. Materials and Methods: Sixty patients with metastatic or unresectable ESCC treated with nivolumab (n=48) or pembrolizumab (n=12) as ≥ second-line treatment between 2016 and 2019 at Asan Medical Center were included. Results: The median age of the patients was 68 years (range, 52 to 76 years), and 93.3% were male. Most patients had metastatic disease (81.7%) and had been previously treated with fluoropyrimidines, platinum, and taxane. In 53 patients with measurable disease, the overall response rate and disease control rate were 15.1% and 35.8%, respectively. With a median follow-up duration of 16.0 months, the median progression-free survival (PFS) and overall survival (OS) were 1.9 months (95% confidence interval [CI], 1.54 to 2.19) and 6.4 months (95% CI, 4.77 to 8.11), respectively. After multivariate analysis, recent use of antibiotics, low prognostic nutrition index (< 35.93), high Glasgow Prognosis Score (≥ 1) at baseline, and ≥ 1.4-fold increase in neutrophil-to-lymphocyte ratio after one cycle from baseline were significantly unfavorable factors for both PFS and OS. Younger age (< 65 years) was a significant factor for unfavorable PFS and hyponatremia (< 135 mmol/L) for unfavorable OS. Conclusion The use of ICIs after the failure of chemotherapy showed comparable efficacy in patients with advanced ESCC in real practice; this may be associated with host immune-nutritional status, which could be predicted by clinical and routine laboratory factors.

We examined the anticancer effects of a novel sirtuin inhibitor, MHY2256, on HCT116 human colorectal cancer cells to investigate its underlying molecular mechanisms. MHY2256 significantly suppressed the activity of sirtuin 1 and expression levels of sirtuin 1/2 and stimulated acetylation of forkhead box O1, which is a target protein of sirtuin 1. Treatment with MHY2256 inhibited the growth of the HCT116 (TP53 wild-type), HT-29 (TP53 mutant), and DLD-1 (TP53 mutant) human colorectal cancer cell lines. In addition, MHY2256 induced G0/G1 phase arrest of the cell cycle progression, which was accompanied by the reduction of cyclin D1 and cyclin E and the decrease of cyclin-dependent kinase 2, cyclin-dependent kinase 4, cyclin-dependent kinase 6, phosphorylated retinoblastoma protein, and E2F transcription factor 1. Apoptosis induction was shown by DNA fragmentation and increase in late apoptosis, which were detected using flow cytometric analysis. MHY2256 downregulated expression levels of procaspase-8, -9, and -3 and led to subsequent poly(ADP-ribose) polymerase cleavage. MHY2256-induced apoptosis was involved in the activation of caspase-8, -9, and -3 and was prevented by pretreatment with Z-VAD-FMK, a pan-caspase inhibitor. Furthermore, the autophagic effects of MHY2256 were observed as cytoplasmic vacuolation, green fluorescent protein-light-chain 3 punctate dots, accumulation of acidic vesicular organelles, and upregulated expression level of light-chain 3-II. Taken together, these results suggest that MHY2256 could be a potential novel sirtuin inhibitor for the chemoprevention or treatment of colorectal cancer or both.

Allogeneic natural killer (NK) cell therapy is a potential therapeutic approach for a variety of solid tumors. We established an expansion method for large-scale production of highly purified and functionally active NK cells, as well as a freezing medium for the expanded NK cells. In the present study, we assessed the effect of cryopreservation on the expanded NK cells in regards to viability, phenotype, and anti-tumor activity. NK cells were enormously expanded (about 15,000-fold expansion) with high viability and purity by stimulating CD³⁺ T cell-depleted peripheral blood mononuclear cells (PBMCs) with irradiated autologous PBMCs in the presence of IL-2 and OKT3 for 3 weeks. Cell viability was slightly reduced after freezing and thawing, but cytotoxicity and cytokine secretion were not significantly different. In a xenograft mouse model of hepatocellular carcinoma cells, cryopreserved NK cells had slightly lower anti-tumor efficacy than freshly expanded NK cells, but this was overcome by a 2-fold increased dose of cryopreserved NK cells. In vivo antibody-dependent cell cytotoxicity (ADCC) activity of cryopreserved NK cells was also demonstrated in a SCID mouse model injected with Raji cells with rituximab co-administration. Therefore, we demonstrated that expanded/frozen NK cells maintain viability, phenotype, and anti-tumor activity immediately after thawing, indicating that expanded/frozen NK cells can provide ‘ready-to-use’ cell therapy for cancer patients.
Animals ; Antibody-Dependent Cell Cytotoxicity ; Carcinoma, Hepatocellular ; Cell Survival ; Cell- and Tissue-Based Therapy ; Cryopreservation* ; Freezing ; Heterografts ; Humans* ; Interleukin-2 ; Killer Cells, Natural* ; Methods ; Mice ; Mice, SCID ; Muromonab-CD3 ; Phenotype ; Rituximab

Apigenin (4′,5,7-trihydroxyflavone) is a flavonoid commonly found in many fruits and vegetables such as parsley, chamomile, celery, and kumquats. In the last few decades, recognition of apigenin as a cancer chemopreventive agent has increased. Significant progress has been made in studying the chemopreventive aspects of apigenin both in vitro and in vivo. Several studies have demonstrated that the anticarcinogenic properties of apigenin occur through regulation of cellular response to oxidative stress and DNA damage, suppression of inflammation and angiogenesis, retardation of cell proliferation, and induction of autophagy and apoptosis. One of the most well-recognized mechanisms of apigenin is the capability to promote cell cycle arrest and induction of apoptosis through the p53-related pathway. A further role of apigenin in chemoprevention is the induction of autophagy in several human cancer cell lines. In this review, we discuss the details of apigenin, apoptosis, autophagy, and the role of apigenin in cancer chemoprevention via the induction of apoptosis and autophagy.
Apigenin* ; Apium graveolens ; Apoptosis* ; Autophagy* ; Cell Cycle Checkpoints ; Cell Line ; Cell Proliferation ; Chamomile ; Chemoprevention ; DNA Damage ; Fruit ; Humans ; In Vitro Techniques ; Inflammation ; Oxidative Stress ; Petroselinum ; Rutaceae ; Vegetables