Objective To analyze the factors affecting the prognosis of patients with locally advanced or metastatic non-small cell lung cancer (NSCLC) undergoing immunotherapy and construct an individualized prognostic nomogram prediction model. Methods A retrospective analysis was conducted on the clinical data of 385 patients with driver gene-negative, locally advanced or metastatic NSCLC who received first-line immune checkpoint inhibitors. Univariate and multivariate Cox regression analyses were used to identify prognostic risk factors, and a prognostic nomogram model was established. The predictive performance of the model was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves and area under the curve (AUC), and calibration curves. The cutoff value of the nomogram was calculated to stratify patients by risk. Survival curves were calculated by Kaplan-Meier analysis. Results Age (HR=1.775, 95%CI: 1.265-2.490), degree of differentiation (HR=0.365, 95%CI: 0.257-0.519), low PD-L1 expression (HR=0.661, 95%CI: 0.455-0.960), high PD-L1 expression (HR=0.423, 95%CI: 0.297-0.603), SCC-Ag (HR=1.549, 95%CI: 1.109-2.163), and CONUT score (HR=2.527, 95%CI: 1.797-3.554) were independent risk factors affecting overall survival (OS) of patients with NSCLC undergoing immunotherapy. The nomogram prediction model constructed on the basis of these factors had a C-index of 0.767. Time-dependent ROC curves for survival showed that the AUCs for 1-, 2-, and 3-year OS were 0.830, 0.853, and 0.886, respectively. Calibration curves indicated that the nomogram-predicted survival rates were in good agreement with the actual outcomes. The cutoff value for the study’s nomogram prediction model was 136.60 points, and survival curves showed statistically significant differences between different risk groups (P<0.05). Conclusion The nomogram model established in this study can effectively predict the prognosis of patients with driver gene-negative locally advanced or metastatic NSCLC treated with first-line immunosuppressive therapy. It provides a new tool for assessing prognosis and aids clinicians in formulating individualized treatment plans.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Cuproptosis, a recently discovered form of regulated cell death involving copper ion metabolism, has emerged as a promising approach for tumor therapy. This pathway not only directly eliminates tumor cells but also promotes immunogenic cell death (ICD), reshaping the tumor microenvironment (TME) and initiating robust anti-tumor immune responses. However, translating cuproptosis-based therapies into clinical applications is hindered by challenges, including complex metabolic regulation, TME heterogeneity, and the precision required for effective drug delivery. To address these limitations, nanoparticles offer transformative solutions by providing precise delivery of cuproptosis-inducing agents, controlled drug release, and enhanced therapeutic efficacy through simultaneous modulation of metabolic pathways and immune responses. This review systematically discusses recent advancements in nanoparticle-based cuproptosis delivery systems, highlighting nanoparticle design principles and their synergistic effects when integrated with other therapeutic modalities such as ICB, PTT, and CDT. Furthermore, we explore the potential of cuproptosis-based nanomedicine for personalized cancer treatment by emphasizing strategies for TME stratification and therapeutic optimization tailored to patient profiles. By integrating current insights from metabolic reprogramming, tumor immunotherapy, and nanotechnology, this review aims to facilitate the clinical translation of cuproptosis nanomedicine and significantly contribute to the advancement of precision oncology.

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult

In this study, high-throughput promoter screening was employed to optimize the heterologous expression of Mesorhizobium loti carbonic anhydrase (MlCA) in order to reduce the costs associated with carbon capture and storage (CCS). To simplify the complexity of traditional vectors, a fusion protein expression system was constructed using superfolder green fluorescent protein (sfGFP) and MlCA. The synthetic promoter library in Escherichia coli was utilized for efficient one-step screening. Based on fluorescence intensity on agar plates, a total of 143 monoclonal colonies were identified, forming a library with varying expression levels. The top four recombinants with the highest fluorescence intensity were selected, among which MlCA driven by the promoter 342042/+ exhibited the highest enzymatic activity, with a specific activity of the 34.6 Wilbur-Anderson units (WAU)/mg. Optimization experiments revealed that MlCA exhibited the best performance when cultured for 4 days under pH 7.0 and 40 ℃ conditions. The Michaelis constant (K_m·hdy) and maximum reaction rate (V_max·hdy) for CO₂ hydration were determined to be 62.46 mmol/L and 0.164 mmol/(s·L), respectively. For esterase hydrolysis, MlCA showed the K_m and V_max of 639.8 mmol/L and 0.035 mmol/(s·L), respectively. MlCA accelerated the CO₂ hydration process, promoting CO₂ mineralized into CaCO₃ within 9 min at low pH and room temperature conditions. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses confirmed that the precipitated product was calcite. This study provides a low-cost and environmentally friendly alternative for future CCS applications.
Carbonic Anhydrases/biosynthesis* ; Promoter Regions, Genetic/genetics* ; Escherichia coli/metabolism* ; Carbon Sequestration ; Carbon Dioxide/metabolism* ; Green Fluorescent Proteins/metabolism*

Objective To clone and express the heat shock cognate protein 20 (SjHsc20) of Schistosoma japonicum, and to preliminarily investigate its biological characteristics. Methods The target fragment of the SjHsc20 gene was amplified using PCR assay and cloned into the pET-28a(+) expression plasmid to generate the recombinant expression vector pET-28a(+)-SjH-sc20, which was then transformed into Escherichia coli BL21 (DE3) competent cells. The recombinant SjHsc20 (rSjHsc20) protein was induced with isopropyl β-D-thiogalactopyranoside (IPTG) and purified, and the expression of the rSjHsc20 protein was checked with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The immunogenicity of the rSjHsc20 protein was detected using Western blotting, and the transcriptional levels of SjHsc20 were quantified in S. japonicum worms at different developmental stages and in male and female adult worms using real-time quantitative PCR (RT-qPCR) assay. Thirty female BALB/c mice at ages 6 to 8 weeks were divided into three groups, including the rSjHsc20 immunization group, the PBS control group, and the ISA 206 adjuvant group, of 10 mice in each group. Mice in the rSjHsc20 immunization group were subcutaneously immunized with 20 μg rSjHsc20 on days 1, 15 and 31, and animals in the PBS control group were subcutaneously injected with the same volume of PBS on days 1, 15 and 31, while mice in the ISA 206 adjuvant group were subcutaneously immunized with the same volume of ISA 206 adjuvant on days 1, 15 and 31, respectively. All mice in each group were infected with (40 ± 2) S. japonicum cercariae via the abdomen 14 day following the last immunization. Levels of serum specific IgG and its subtypes IgG1 and IgG2 antibodies against rSjHsc20, and the serum titers of anti-rSjHsc20 antibody were detected in mice using indirect enzyme-linked immunosorbent assay (ELISA). All mice were sacrifice 42 days post-infection, and S. japonicum worms were collected from the hepatic portal vein and counted. The eggs per gram (EPG), worm burden reductions and egg burden reductions were estimated to evaluate the protective efficacy of the rSjHsc20 protein. Results The SjHsc20 gene had an open reading frame (ORF) with 756 bp in length and encoded 252 amino acids, and the rSjHsc20 protein had a relative molecular mass of approximately 29 kDa. The rSjHsc20 protein was recognized by the serum of mice infected with S. japonicum and the serum of mice immunized with the rSjHsc20 protein, indicating that rSjHsc20 had a good immunogenicity. There was a significant difference in the transcriptional levels of the SjHsc20 gene among the 7-day (1.001 4 ± 0.065 7), 12-day (2.268 3 ± 0.129 2), 21-day (1.378 5 ± 0.160 4), 28-day (1.196 4 ± 0.244 0), 35-day (1.646 3 ± 0.226 1), 42-day worms of S. japonicum (1.758 0 ± 0.611 1) (F = 38.45, P < 0.000 1), and the transcriptional level of the SjHsc20 gene was higher in the 12-day worms than in worms at other developmental stages (all P values < 0.000 1). The serum levels of anti-rSjHsc20 IgG antibody were 0.106 6 ± 0.010 7, 0.108 3 ± 0.010 4, and 0.553 2 ± 0.069 1 in the PBS control group, ISA 206 adjuvant group, and rSjHsc20 immunization group following the last immunization, respectively, and the serum levels of IgG1 antibody were 0.137 3 ± 0.054 0, 0.181 1 ± 0.096 8, and 1.765 8 ± 0.221 1, while the levels of IgG2a antibody were 0.280 3 ± 0.197 6, 0.274 0 ± 0.146 3, and 1.560 4 ± 0.106 0, respectively. There were significant differences in the serum levels of anti-rSjHsc20 IgG (F = 397.70, P < 0.000 1), IgG1 (F = 401.00, P < 0.000 1) and IgG2a antibodies (F = 229.70, P < 0.000 1) among the three groups, and the serum levels of anti-rSjHsc20 IgG, IgG1 and IgG2a antibodies were higher in the rSjHsc20 immunization group than in the PBS control group and the ISA 206 adjuvant group (all P values < 0.000 1). There was a significant difference in the IgG1/IgG2a ratio among the rSjHsc20 immunization group (1.177 2 ± 0.143 6), the PBS control group (0.428 4 ± 0.199 8) and the ISA 206 adjuvant group (0.559 9 ± 0.181 1) (F = 43.97, P < 0.000 1), and the IgG1/IgG2a ratio was > 1 in the rSjHsc20 immunization group, which was higher than in the PBS control group and the ISA 206 adjuvant group (both P values < 0.000 1). The titers of serum anti-rSjHsc20 antibody were all above 1∶16 384 in the rSjHsc20 immunization group following immunizations on days 1, 15 and 31, indicating that the rSjHsc20 protein had a strong immunogenicity. The mean worm burdens were (16.60±5.75), (15.80±5.58) worms per mouse and (14.40±5.75) worms per mouse in the PBS control group, the ISA 206 adjuvant group and the rSjHsc20 immunization group 42 days post-infection with S. japonicum cercariae (F = 0.50, P > 0.05), and the EPG were 68 370 ± 22 690, 67 972 ± 19 502, and 41 075 ± 13 251 in the PBS control group, the ISA 206 adjuvant group and the rSjHsc20 immunization group (F = 4.55, P < 0.05), with lower EPG in the PBS control group and the ISA 206 adjuvant group than in the rSjHsc20 immunization group (both P values < 0.05). Immunization with the rSjHsc20 protein resulted in a worm burden reduction of 13.25% and an egg burden reduction of 39.92% relative to the PBS control group. Conclusions SjHsc20 is successfully cloned and expressed, and the rSjHsc20 protein induces partial immunoprotective effects in mice, which provides a basis for deciphering the biological functions of SjHsc20 and assessing the potential of SjH-sc20 as a vaccine candidate.