Recently, photodynamic therapy (PDT) has gained considerable attention as a promising therapeutic approach for the treatment of psoriasis. Unfortunately, the activation of high mobility group box 1 protein (HMGB1) by PDT triggers innate and adaptive immune responses, which exacerbate skin inflammation. Herein, we combined glycyrrhizic acid (GA), a natural anti-inflammatory compound and immunomodulator derived from the herb Glycyrrhiza uralensis Fisch., with PDT actuated by the photosensitizer chlorin e6 (Ce6) by co-loading them in GA-based lipid nanoparticles coated with a catechol-modified quaternary chitosan salt (GC NPs/QCS-C). GC NPs/QCS-C exhibited high drug loading efficacy, uniform size distribution, an ideal topical adhesive property, enhanced skin retention and penetration in psoriasis-like lesions, and high intracellular uptake in epidermal cells compared with the counterparts. Subsequently, the transdermal administration of GC NPs/QCS-C followed by near-infrared laser radiation in an imiquimod-induced psoriasis-like mouse model significantly ameliorated psoriasis symptoms, promoted the apoptosis of hyperproliferative epidermal cells, and alleviated the inflammatory cascade. The significant therapeutic outcomes of GC NPs/QCS-C were attributed to the synergistic effects of GA and PDT on modulating immune cell recruitment and inhibiting dendritic cell maturation. Our results demonstrated that the topical bio-adhesive nanosystem that combines GA and Ce6 offers a synergistic chemo-phototherapeutic strategy for psoriasis treatment.

Objective:To analyze the quality level of maternal mid-trimester serum prenatal screening in China from 2019 to 2023.Methods:A questionnaire survey was conducted to collect information from laboratories participating in the National Health Commission′s Clinical Laboratory Center inter-laboratory quality assessment program for prenatal screening from 2019 to 2023. The collected data included screening protocols, detection methods, testing systems, and monthly laboratory screening quality indicators. The Chi-square test was used to compare the initial screening positive rates among different screening protocols, provinces, detection methods, and testing systems. A log-transformed linear regression analysis was performed to evaluate the relationship between the average annual sample size and the out-of-control rate of various median multiple of the median (mMoM) values.Results:This study included 806 laboratories from 29 provinces (including autonomous regions and municipalities). The five-year average out-of-control rates for mMoM values across indicators ranged from 15.8% to 31.3%. The initial positive rates of dual, triple, and quadruple screening protocols were statistically different ( χ2=760.2, P<0.001). The initial positive rates across different provinces ranged from 4.7% to 10.3%, with statistically significant differences ( χ2=35 388.0, P<0.001). There were also statistically significant differences in initial positive rates between different testing systems ( χ2=2 493.2, P<0.001). Testing systems using chemiluminescence methods had significantly higher initial positive rates compared to systems using time-resolved fluorescence methods (7.2% vs. 6.5%, χ2=533.6, P<0.001). Log-linear regression analysis showed that testing systems with larger annual average sample sizes had lower out-of-control rates for mMoM values (AFP: β=-0.100, P=0.005; hCG: β=-0.123, P=0.008; uE3: β=-0.139, P=0.007). Conclusion:There are significant differences in the quality of maternal mid-trimester serum screening across different detection methods, testing systems and provinces in China.

Objective To investigate the therapeutic mechanism of Guiyuan Decoction(GYD,with Rehmanniae Radix as the principal herb)in Sj?gren's syndrome(SS).Methods Active components of GYD were retrieved from network pharmacology databases,and potential targets for SS were predicted.Common targets were identified via Venn analysis,followed by construction of a"herb-compound-target"network.Protein-protein interaction(PPI)networks were generated,and Gene Ontology(GO)functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses were performed using the DAVID platform.Molecular docking was employed to evaluate binding affinities between core components and key target proteins.In vivo and in vitro experiments were conducted to validate predictions based on literature and docking results.Results A total of 318 bioactive components in GYD and 148 disease-related overlapping targets were identified.GO and KEGG analyses yielded 921 functional terms and 159 signaling pathways,respectively.Molecular docking revealed stigmasterol exhibited the strongest binding affinity with caspase-3(CASP3),while quercetin,kaempferol,andβ-sitosterol also showed robust interactions with core targets.Experimental validation demonstrated that both GYD and its active constituent stigmasterol significantly downregulated protein expression levels of CASP3,NLR family pyrin domain containing 3(NLRP3),apoptosis-associated speck-like protein containing a CARD(ASC),interleukin-1β(IL-1 β),and Caspase-1 in submandibular gland tissues of Sj?gren's syndrome mice and in lipopolysaccharide(LPS)-stimulated submandibular gland cells.Furthermore,they effectively reduced the concentrations of interleukin-6(IL-6),interleukin-1β(IL-1 β),and tumor necrosis factor-α(TNF-α)in peripheral blood and cell culture supernatants of Sj?gren's syndrome mice.Conclusion Integrated network pharmacology and molecular docking suggest that GYD exerts anti-SS effects primarily via stigmasterol-mediated modulation of CASP3.Experimental evidence confirms that GYD and stigmasterol attenuate inflammatory responses and may ameliorate SS by targeting CASP3 to suppress NLRP3 inflammasome activation.

Objective:To investigate the current status of preimplantation genetic testing for aneuploidies (PGT-A) in China in 2024 and to provide recommendations for ensuring the consistency of PGT-A results.Methods:This study was a nationwide external quality assessment research. The National Center for Clinical Laboratories of the National Health Commission conducted two surveys in June and December 2024, with participation from 31 laboratories across China. During each survey, quality control samples of varying concentrations were distributed: the first survey distributed 6 samples (Batch Nos. 202401-202406) and the second survey distributed 3 samples (Batch Nos. 202431-202433). Participating laboratories were required to submit information including detection platforms, single-cell amplification methods, library construction methods, sequencing instruments, and test results. Collected data were statistically analyzed using Microsoft Excel to evaluate laboratories' PGT-A testing capabilities and determine whether results met predefined quality control requirements.Results:All 31 laboratories submitted results for both surveys. In the first survey, 90.3% (28/31) met qualification criteria with a 9.7% failure rate (3/31). For individual batches, pass rates were 90.3% (28/31) for batches 202401-202403 and 100% (31/31) for batches 202404-202406. The second survey achieved 100% (31/31) compliance across all three batches (202431-202433), with each batch maintaining a 100% pass rate.Conclusion:The detection quality of PGT-A in China is generally good, but with differences, and factors such as sample concentration, transportation conditions and detection methods affect the consistency of the results. The study highlights the urgent need for nationwide external quality assessment of PGT-A in China. This will help identify issues in laboratories in a timely manner and ensure the consistency of PGT-A test results.

Tissue resident memory T(TRM)cells are a class of memory T cells with long life span and non-lymphoid resident characteristics.TRM cells are the first line of defense against secondary infections in human.TRM cells can restrict the transmission of viruses as well as combat against viruses.Rapid evolution of severe acute respiratory syn-drome coronavirus 2(SARS-CoV-2)and post-COVID-19 syndrome could lead to a series of symptoms such as pulmonary fi-brosis,resulting in long-term fall in quality of life.Many studies focused on enhancing the ability to cope with the mutation of SARS-CoV-2 and reducing the incidence of post-COVID-19 syndrome.The relationship between TRM cells and SARS-CoV-2 infection or therapy still remain unknown.It is significant to investigate TRM cells divergence and functions for the development of vaccines.We review the functions and residence properties of TRM cells,as well as their roles in SARS-CoV-2 infection and post-COVID-19 syndrome.In this review,we are dedicated to deepen our understanding of the immune process in SARS-CoV-2 infection and provide a new prospect for the prevention and treatment of post-COVID-19 syndrome.

Purpose To explore the application value of deep learning in the prediction of hepatocellular carcinoma microvascular invasion based on CT images.Materials and Methods A total of 63 hepatocellular carcinoma patients with pathologic diagnosis proving microvascular invasion status from January 2020 to December 2022 in Chifeng City Hospital were included,and preoperative enhanced CT images of these patients were collected,and randomly divided into training,validation and testing groups.Three classical convolutional neural network models(AlexNet,VGG16 and ResNet50)were established by transfer learning.The general clinical data of hepatocellular carcinoma patients were analyzed by single-factor and multifactor Logistic regression to establish clinical prediction models.The optimal model was obtained by comparing the actual prediction effects of the four prediction models.Results The area under the curve(AUC)of clinical prediction model was 0.844,sensitivity was 0.833 and specificity was 0.708.The AUC of AlexNet model was 0.865,sensitivity was 0.833,specificity was 0.717 and training time was 818.2 s.The AUC of VGG16 model was 0.892,sensitivity was 0.857,specificity was 0.717 and training time was 9 743.2 s.The AUC of ResNet50 model was 0.937,sensitivity was 0.786,specificity was 0.925 and training time was 3 800.8 s.Conclusion Deep learning modeling is an assessment method for preoperative noninvasive prediction of microvascular invasion,which is superior to clinical prediction models,with the best performance of the ResNet50 model.It can provide a strong reference for physicians in making clinical decisions.

Objective:To investigate the current status of preimplantation genetic testing for aneuploidies (PGT-A) in China in 2024 and to provide recommendations for ensuring the consistency of PGT-A results.Methods:This study was a nationwide external quality assessment research. The National Center for Clinical Laboratories of the National Health Commission conducted two surveys in June and December 2024, with participation from 31 laboratories across China. During each survey, quality control samples of varying concentrations were distributed: the first survey distributed 6 samples (Batch Nos. 202401-202406) and the second survey distributed 3 samples (Batch Nos. 202431-202433). Participating laboratories were required to submit information including detection platforms, single-cell amplification methods, library construction methods, sequencing instruments, and test results. Collected data were statistically analyzed using Microsoft Excel to evaluate laboratories' PGT-A testing capabilities and determine whether results met predefined quality control requirements.Results:All 31 laboratories submitted results for both surveys. In the first survey, 90.3% (28/31) met qualification criteria with a 9.7% failure rate (3/31). For individual batches, pass rates were 90.3% (28/31) for batches 202401-202403 and 100% (31/31) for batches 202404-202406. The second survey achieved 100% (31/31) compliance across all three batches (202431-202433), with each batch maintaining a 100% pass rate.Conclusion:The detection quality of PGT-A in China is generally good, but with differences, and factors such as sample concentration, transportation conditions and detection methods affect the consistency of the results. The study highlights the urgent need for nationwide external quality assessment of PGT-A in China. This will help identify issues in laboratories in a timely manner and ensure the consistency of PGT-A test results.

Objective:To analyze the quality level of maternal mid-trimester serum prenatal screening in China from 2019 to 2023.Methods:A questionnaire survey was conducted to collect information from laboratories participating in the National Health Commission′s Clinical Laboratory Center inter-laboratory quality assessment program for prenatal screening from 2019 to 2023. The collected data included screening protocols, detection methods, testing systems, and monthly laboratory screening quality indicators. The Chi-square test was used to compare the initial screening positive rates among different screening protocols, provinces, detection methods, and testing systems. A log-transformed linear regression analysis was performed to evaluate the relationship between the average annual sample size and the out-of-control rate of various median multiple of the median (mMoM) values.Results:This study included 806 laboratories from 29 provinces (including autonomous regions and municipalities). The five-year average out-of-control rates for mMoM values across indicators ranged from 15.8% to 31.3%. The initial positive rates of dual, triple, and quadruple screening protocols were statistically different ( χ2=760.2, P<0.001). The initial positive rates across different provinces ranged from 4.7% to 10.3%, with statistically significant differences ( χ2=35 388.0, P<0.001). There were also statistically significant differences in initial positive rates between different testing systems ( χ2=2 493.2, P<0.001). Testing systems using chemiluminescence methods had significantly higher initial positive rates compared to systems using time-resolved fluorescence methods (7.2% vs. 6.5%, χ2=533.6, P<0.001). Log-linear regression analysis showed that testing systems with larger annual average sample sizes had lower out-of-control rates for mMoM values (AFP: β=-0.100, P=0.005; hCG: β=-0.123, P=0.008; uE3: β=-0.139, P=0.007). Conclusion:There are significant differences in the quality of maternal mid-trimester serum screening across different detection methods, testing systems and provinces in China.

Tissue resident memory T(TRM)cells are a class of memory T cells with long life span and non-lymphoid resident characteristics.TRM cells are the first line of defense against secondary infections in human.TRM cells can restrict the transmission of viruses as well as combat against viruses.Rapid evolution of severe acute respiratory syn-drome coronavirus 2(SARS-CoV-2)and post-COVID-19 syndrome could lead to a series of symptoms such as pulmonary fi-brosis,resulting in long-term fall in quality of life.Many studies focused on enhancing the ability to cope with the mutation of SARS-CoV-2 and reducing the incidence of post-COVID-19 syndrome.The relationship between TRM cells and SARS-CoV-2 infection or therapy still remain unknown.It is significant to investigate TRM cells divergence and functions for the development of vaccines.We review the functions and residence properties of TRM cells,as well as their roles in SARS-CoV-2 infection and post-COVID-19 syndrome.In this review,we are dedicated to deepen our understanding of the immune process in SARS-CoV-2 infection and provide a new prospect for the prevention and treatment of post-COVID-19 syndrome.

Purpose To explore the application value of deep learning in the prediction of hepatocellular carcinoma microvascular invasion based on CT images.Materials and Methods A total of 63 hepatocellular carcinoma patients with pathologic diagnosis proving microvascular invasion status from January 2020 to December 2022 in Chifeng City Hospital were included,and preoperative enhanced CT images of these patients were collected,and randomly divided into training,validation and testing groups.Three classical convolutional neural network models(AlexNet,VGG16 and ResNet50)were established by transfer learning.The general clinical data of hepatocellular carcinoma patients were analyzed by single-factor and multifactor Logistic regression to establish clinical prediction models.The optimal model was obtained by comparing the actual prediction effects of the four prediction models.Results The area under the curve(AUC)of clinical prediction model was 0.844,sensitivity was 0.833 and specificity was 0.708.The AUC of AlexNet model was 0.865,sensitivity was 0.833,specificity was 0.717 and training time was 818.2 s.The AUC of VGG16 model was 0.892,sensitivity was 0.857,specificity was 0.717 and training time was 9 743.2 s.The AUC of ResNet50 model was 0.937,sensitivity was 0.786,specificity was 0.925 and training time was 3 800.8 s.Conclusion Deep learning modeling is an assessment method for preoperative noninvasive prediction of microvascular invasion,which is superior to clinical prediction models,with the best performance of the ResNet50 model.It can provide a strong reference for physicians in making clinical decisions.