Osteoporosis is a systemic metabolic disease characterized by decreased bone mass, leading to an increased risk of fractures. Although osteoporosis in children and adolescents is rare, its incidence in younger populations is showing an increasingly notable trend. The diagnostic criteria for osteoporosis in children and adolescents include a bone mineral density(BMD) Z-score of≤-2.0 accompanied by a significant fracture history, defined as two or more long bone fractures before the age of 10, three or more long bone fractures before the age of 19, or the presence of low-energy vertebral compression fractures even in the absence of low BMD. The genetic causes and underlying mechanisms of pediatric osteoporosis remain largely unknown, requiring further research to elucidate the molecular pathways involved. Such advances could help reduce the disease′s impact on growth and development and improve the quality of life in affected children and adolescents.

Objective:To explore the clinical application value of artificial intelligence（AI）-assisted training in enhancing the accuracy of nuchal translucency（NT）identification，standardization of measurement，and diagnostic efficacy for abnormalities among ultrasound residents.Methods:A retrospective collection of 300 standard fetal NT ultrasound images was conducted at the Center for Medical Ultrasound，Suzhou Hospital Affiliated of Nanjing Medical University from January 2018 to June 2024. The AI model performed NT measurements and diagnoses once. Four sonographers of different seniority levels（including two resident physicians）independently conducted NT measurements and diagnoses twice. Prior to the experiment，the middle-age and resident sonographers had uniformly completed traditional theory training. Following the first independent measurements，the two resident sonographers received additional AI-assisted training，after which all 4 sonographers performed the second independent measurements. A fetal medicine expert evaluated blindly all the results and compared the differences in NT recognition accuracy，measurement standard rate and diagnosis accuracy between the middle-age sonographer（traditional training only）and two resident sonographers（traditional + AI-assisted training）.Results:For the middle-aged sonographer who only received traditional lecture-based training，the accuracy of NT recognition，standardization rate of measurement，or diagnostic accuracy were not significantly improved befroe and after the training，and the diffrence was not statistically significant（ χ2=0.189，1.887，0.326；all P>0.05）. In contrast，the second-year resident（Resident 2）and first-year resident（Resident 1），who received both traditional lecture-based training and AI training，demonstrated some improvements in the accuracy of NT measurement site recognition，though the differences were not statistically significant（ χ2=1.301，2.418；all P>0.05）. However，both residents did significant improvements in the standardization rate of NT measurement（ χ2=25.768，17.035；all P<0.05）. In terms of diagnostic accuracy，Resident 1 did significant improvement（ χ2=10.180， P<0.05），while Resident 2 also did some improvement，though the difference was not statistically significant（ χ2=2.573， P>0.05）. Conclusions:The AI-assisted training system enhances the ability of ultrasound resident sonographers to recognize，measure，and diagnose NT，providing a novel and efficient training model for standardized residency training in ultrasound specialties.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Aim To explore the effects of D-limonene on the steatosis of primary mouse hepatocytes and its potential mechanism of action.Methods Oleic acid-induced steatosis in primary mouse hepatocytes was used as a model to observe the effects of D-limonene on cell viability,cellular lipid content,and intracellular expression of proteins such as AMP-activated protein kinase(AMPK),acetyl-coenzyme A carboxylase 1(ACC1),and carnitine palmitoyl transferase 1A(CPT1A).Results It was found that a low dose of D-limonene could effectively enhance the viability of primary mouse hepatocytes.When oleic acid at a con-centration of 300 μmol·L-1 successfully induced steatosis in primary mouse hepatocytes,D-limonene re-duced the lipid content of the cells,and D-limonene up-regulated the cellular AMPK expression level,down-regulated the cellular ACC1 and fatty acid synthetase(FAS)expression levels,which in turn promoted the overexpression of CPT1A.Conclusions D-limonene has the effect of reducing lipid deposition in primary mouse hepatocytes,and the mechanisms may be related to the activation of AMPK,the inhibitions of ACC1 and FAS,and the up-regulation of CPT1A protein expres-sion level.

Aim To explore the effects of D-limonene on the steatosis of primary mouse hepatocytes and its potential mechanism of action.Methods Oleic acid-induced steatosis in primary mouse hepatocytes was used as a model to observe the effects of D-limonene on cell viability,cellular lipid content,and intracellular expression of proteins such as AMP-activated protein kinase(AMPK),acetyl-coenzyme A carboxylase 1(ACC1),and carnitine palmitoyl transferase 1A(CPT1A).Results It was found that a low dose of D-limonene could effectively enhance the viability of primary mouse hepatocytes.When oleic acid at a con-centration of 300 μmol·L-1 successfully induced steatosis in primary mouse hepatocytes,D-limonene re-duced the lipid content of the cells,and D-limonene up-regulated the cellular AMPK expression level,down-regulated the cellular ACC1 and fatty acid synthetase(FAS)expression levels,which in turn promoted the overexpression of CPT1A.Conclusions D-limonene has the effect of reducing lipid deposition in primary mouse hepatocytes,and the mechanisms may be related to the activation of AMPK,the inhibitions of ACC1 and FAS,and the up-regulation of CPT1A protein expres-sion level.

Osteoporosis is a systemic metabolic disease characterized by decreased bone mass, leading to an increased risk of fractures. Although osteoporosis in children and adolescents is rare, its incidence in younger populations is showing an increasingly notable trend. The diagnostic criteria for osteoporosis in children and adolescents include a bone mineral density(BMD) Z-score of≤-2.0 accompanied by a significant fracture history, defined as two or more long bone fractures before the age of 10, three or more long bone fractures before the age of 19, or the presence of low-energy vertebral compression fractures even in the absence of low BMD. The genetic causes and underlying mechanisms of pediatric osteoporosis remain largely unknown, requiring further research to elucidate the molecular pathways involved. Such advances could help reduce the disease′s impact on growth and development and improve the quality of life in affected children and adolescents.

Objective:To explore the clinical application value of artificial intelligence（AI）-assisted training in enhancing the accuracy of nuchal translucency（NT）identification，standardization of measurement，and diagnostic efficacy for abnormalities among ultrasound residents.Methods:A retrospective collection of 300 standard fetal NT ultrasound images was conducted at the Center for Medical Ultrasound，Suzhou Hospital Affiliated of Nanjing Medical University from January 2018 to June 2024. The AI model performed NT measurements and diagnoses once. Four sonographers of different seniority levels（including two resident physicians）independently conducted NT measurements and diagnoses twice. Prior to the experiment，the middle-age and resident sonographers had uniformly completed traditional theory training. Following the first independent measurements，the two resident sonographers received additional AI-assisted training，after which all 4 sonographers performed the second independent measurements. A fetal medicine expert evaluated blindly all the results and compared the differences in NT recognition accuracy，measurement standard rate and diagnosis accuracy between the middle-age sonographer（traditional training only）and two resident sonographers（traditional + AI-assisted training）.Results:For the middle-aged sonographer who only received traditional lecture-based training，the accuracy of NT recognition，standardization rate of measurement，or diagnostic accuracy were not significantly improved befroe and after the training，and the diffrence was not statistically significant（ χ2=0.189，1.887，0.326；all P>0.05）. In contrast，the second-year resident（Resident 2）and first-year resident（Resident 1），who received both traditional lecture-based training and AI training，demonstrated some improvements in the accuracy of NT measurement site recognition，though the differences were not statistically significant（ χ2=1.301，2.418；all P>0.05）. However，both residents did significant improvements in the standardization rate of NT measurement（ χ2=25.768，17.035；all P<0.05）. In terms of diagnostic accuracy，Resident 1 did significant improvement（ χ2=10.180， P<0.05），while Resident 2 also did some improvement，though the difference was not statistically significant（ χ2=2.573， P>0.05）. Conclusions:The AI-assisted training system enhances the ability of ultrasound resident sonographers to recognize，measure，and diagnose NT，providing a novel and efficient training model for standardized residency training in ultrasound specialties.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Recent studies revealed the relationship among homologous recombination repair (HRR), androgen receptor (AR), and poly(adenosine diphosphate-ribose) polymerase (PARP); however, the synergy between anti-androgen enzalutamide (ENZ) and PARP inhibitor olaparib (OLA) remains unclear. Here, we showed that the synergistic effect of ENZ and OLA significantly reduced proliferation and induced apoptosis in AR-positive prostate cancer cell lines. Next-generation sequencing followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed the significant effects of ENZ plus OLA on nonhomologous end joining (NHEJ) and apoptosis pathways. ENZ combined with OLA synergistically inhibited the NHEJ pathway by repressing DNA-dependent protein kinase catalytic subunit (DNA-PKcs) and X-ray repair cross complementing 4 (XRCC4). Moreover, our data showed that ENZ could enhance the response of prostate cancer cells to the combination therapy by reversing the anti-apoptotic effect of OLA through the downregulation of anti-apoptotic gene insulin-like growth factor 1 receptor ( IGF1R ) and the upregulation of pro-apoptotic gene death-associated protein kinase 1 ( DAPK1 ). Collectively, our results suggested that ENZ combined with OLA can promote prostate cancer cell apoptosis by multiple pathways other than inducing HRR defects, providing evidence for the combined use of ENZ and OLA in prostate cancer regardless of HRR gene mutation status.
Male ; Humans ; Prostatic Neoplasms, Castration-Resistant/genetics* ; Drug Resistance, Neoplasm/genetics* ; Cell Line, Tumor ; Receptors, Androgen/genetics* ; Nitriles ; Apoptosis

Objectives:To evaluate the role of pancreas multidisciplinary team(MDT) clinic in the diagnosis of pancreatic diseases,patient compliance with MDT advice,and the impact of MDT on the postoperative survival of patients with pancreatic cancer.Methods:The study included 927 patients(554 males,373 females,aged (58.1±13.3)years (range: 15 to 89 years)) that had visited the pancreas MDT clinic of Zhongshan Hospital from May 2015 to December 2021,and 677 patients(396 males, 281 females, aged (63.6±8.9)years(range: 32 to 95 years)) who underwent radical surgery and with pathologically confirmed pancreatic adenocarcinoma from January 2012 to December 2020,of whom 79 patients had attended the pancreas MDT. The clinical and pathological data were collected and analyzed retrospectively. Diseases were classified in accordance with 2010 WHO classification of tumors of the digestive system and usual clinical practices. The Kaplan-Meier method was used for drawing the survival curve and calculating the survival rate. The univariate analysis was done by Log-rank test and the multivariate analysis was done by COX proportional hazards model. Survival rates were compared using χ 2 test. Results:Among the 927 patients that had visited the MDT clinic,233 patients(25.1%) were referred due to undetermined diagnosis. A direct diagnosis was made in 109 cases (46.8%,109/233) by the MDT clinic, of which 98 were consistent with the final diagnosis,resulting in an accuracy of 89.9%(98/109). The direct diagnosis rate in the recent years(36.6%(41/112),from June 2019 to December 2021) decreased compared to that in the previous years(56.2%(68/121),from May 2015 to May 2019),yet the accuracy in the recent years(90.2%,37/41) was basically the same as before (89.7%,61/68). The rate of compliance of the entire cohort was 71.5%(663/927), with the compliance rate in the recent two and a half years(81.4%,338/415) remarkably higher than that in the previous four years(63.4%,325/512). Patients with pancreatic cancer that attended the MDT exhibited a trend toward longer median postoperative survival than patients that did not attend the MDT,but the difference was not statistically significant(35.2 months vs.30.2 months, P>0.05). The 1-year and 3-year survival rates of patients that attended the MDT were significanly higher than patients that did not attend the MDT(88.6% vs. 78.4%, P<0.05;32.9% vs. 21.9%, P<0.05,respectively),but the 5-year survival rate was not statistically different(7.6% vs. 4.8%, P>0.05). Conclusions:The pancreas MDT clinic is an accurate and convenient way to diagnose intractable pancreatic diseases,and in the recent years the patients′ compliance rate with MDT advice has increased. Pancreatic cancer patients that have attended the MDT have higher 1-year and 3-year postoperative survival rates,but the long-term survival benefits of MDT still needs to be proved by clinical studies on a larger scale.