To elucidate the mechanism by which steaming affects the quality of Curcuma kwangsiensis root tubers, methods such as LSCM, RVA, dual-wavelength spectrophotometry, LF-NMR, and LC-MS were employed to qualitatively and quantitatively detect changes in starch gelatinization characteristics, water distribution, and material composition of C. kwangsiensis root tubers under different steaming durations. Based on multivariate statistical analysis, the correlation between differences in gelatinization parameters, water distribution, and terpenoid material composition was investigated. The results indicate that steaming affects both starch gelatinization and water distribution in C. kwangsiensis. During the steaming process, transformations occur between amylose and amylopectin, as well as between semi-bound water and free water. After 60 min of steaming, starch gelatinization and water distribution reached an equilibrium state. The content of amylopectin, the amylose-to-amylopectin ratio, and parameters such as gelatinization temperature, viscosity, breakdown value, and setback value were significantly correlated(P≤0.05). Additionally, the amylose-to-amylopectin ratio was significantly correlated with total free water and total water content(P≤0.05). Steaming induced differences in the material composition of C. kwangsiensis root tubers. Clustering of primary metabolites in the OPLS-DA model was distinct, while secondary metabolites were classified into 9 clusters using the K-means clustering algorithm. Differential terpenoid metabolites such as(-)-α-curcumene were significantly correlated with zerumbone, retinal, and all-trans-retinoic acid(P<0.05). Curcumenol was significantly correlated with isoalantolactone and ursolic acid(P<0.05), while all-trans-retinoic acid was significantly correlated with both zerumbone and retinal(P<0.05). Alpha-tocotrienol exhibited a significant correlation with retinal and all-trans-retinoic acid(P<0.05). Amylose was extremely significantly correlated with(-)-α-curcumene, curcumenol, zerumbone, retinal, all-trans-retinoic acid, and α-tocotrienol(P<0.05). Amylopectin was significantly correlated with zerumbone(P<0.05) and extremely significantly correlated with(-)-α-curcumene, curcumenol, zerumbone, retinal, all-trans-retinoic acid, and 9-cis-retinoic acid(P<0.01). The results provide scientific evidence for elucidating the mechanism of quality formation of steamed C. kwangsiensis root tubers as a medicinal material.
Curcuma/chemistry* ; Starch/chemistry* ; Multivariate Analysis ; Water/chemistry* ; Terpenes/analysis* ; Plant Roots/chemistry* ; Plant Tubers/chemistry* ; Drugs, Chinese Herbal/chemistry*

This study systematically analyzed the global research landscape, technological composition, and core patents in the field of networks target and network pharmacology, and proposes further suggestions based on the IncoPat patent citation database and VOSviewer bibliometric network visualization tool. Using patent literature metrics and scientific knowledge mapping method, technological innovation pathways, research hotspots, and future directions in this field were further revealed. In particular, this field is moving towards data-driven, intelligent, and systematic approaches. Patent analysis indicated that most patent applications in this domain focused on traditional Chinese medicine(TCM), which have provided key engineering technical approaches to explore and solve complex problems of TCM. By integrating big data and artificial intelligence technologies, network targets and network pharmacology have conferred high-precision screening and quality control of key components and targets in herbal formulations and prescriptions, accelerating the clinical translation and industrialization of TCM-based new drugs and health products with medicine-food homology. Therefore, it is essential to optimize the patent protection system and establish integrated technology platforms in this field for ensuring the competitiveness of technological achievements in research and clinical application. These efforts will advance the widespread application and high-quality development of TCM modernization, precision medicine, and innovative drug discovery.
Bibliometrics ; Patents as Topic ; Humans ; Medicine, Chinese Traditional ; Network Pharmacology/trends* ; Drugs, Chinese Herbal/pharmacology*

This study applied the grading of recommendations assessment, development and evaluation(GRADE) system and the integrated evidence chain-based effectiveness evaluation of traditional Chinese medicine(Eff-iEC) to evaluate the evidence for 12 commonly used Chinese patent medicines for the treatment of osteoporosis, which are frequently recommended in guidelines or expert consensuses. The results showed that Xianling Gubao Capsules/Tablets were rated as C(low-level evidence) according to the GRADE system, and as BA~+B~+(intermediate evidence) according to the Eff-iEC system. Jintiange Capsules were rated as C(low-level evidence) by the GRADE system, and as AA~+B(high-level evidence) by the Eff-iEC system. Gushukang Granules/Capsules were rated as C(low-level evidence) by GRADE system, and as BA~+B~+(intermediate evidence) by Eff-iEC system. Zuogui Pills were rated as C(low-level evidence) by GRADE system, and as AA~(++)B~+(high-level evidence) by Eff-iEC system. Qianggu Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AA~+B~+(high-level evidence) by Eff-iEC system. Zhuanggu Zhitong Capsules were rated as D(extremely low-level evidence) by GRADE system, and as BA~+B(intermediate evidence) by Eff-iEC system. Jingui Shenqi Pills were rated as D(extremely low-level evidence) by GRADE system, and as AA~+B(high-level evidence) by Eff-iEC system. Quanduzhong Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AD~+B~+(low-level evidence) by Eff-iEC system. Epimedium Total Flavones Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AAB~+(high-level evidence) by Eff-iEC system. Yougui Pills were rated as D(extremely low-level evidence) by GRADE system, and as AA~(++)B~(+ )(high-level evidence) by Eff-iEC system. Qigu Capsules were rated as D(extremely low-level evidence) by GRADE system, and as BB~+B(intermediate evidence) by Eff-iEC system. Liuwei Dihuang Pills were rated as C(low-level evidence) by GRADE system, and as AA~(++)B~+(high-level evidence) by Eff-iEC system. Overall, the Eff-iEC system provides a more comprehensive assessment of the effectiveness evidence for traditional Chinese medicine(TCM) than the GRADE system. However, it still has certain limitations that hinder its wider promotion and application. In terms of clinical evidence evaluation, both the Eff-iEC and GRADE systems reflect that the current clinical research quality on Chinese patent medicines for the treatment of osteoporosis is generally low. High-quality clinical trials are still needed in the future to further validate clinical efficacy.
Drugs, Chinese Herbal/therapeutic use* ; Osteoporosis/drug therapy* ; Humans ; Nonprescription Drugs/therapeutic use* ; Evidence-Based Medicine ; Medicine, Chinese Traditional

ObjectiveTo characterize the changes of metabolites of Blumea balsamifera in the process of sweating by non-targeted metabolomics， and to investigate the influence of sweating processing on the constituents of B. balsamifera. MethodsUltra performance liquid chromatography-quadrupole/electrostatic field orbitrap high resolution mass spectrometry（UPLC-Q-Exactive Orbitrap-MS） metabolomics was used to identify the metabolites in no sweating group（F1）， sweating 2 d group（F2） and sweating 4 d group（F3）， the differences of metabolites between the groups were compared by principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， and differential metabolites were screened according to the variable importance in the projection（VIP） value>1 and P<0.05， and the pathway enrichment of the differential metabolites was analyzed by Kyoto Encyclopedia of Genes and Genomes（KEGG）. ResultsThe results of PCA and OPLS-DA showed a clear distinction between the three groups of samples， indicating significant differences in the compositions of the three groups of samples. A total of 433 differential metabolites were screened between the F1 and F2， with 154 up-regulated and 279 down-regulated， the significant up-regulated metabolites were tangeritin， 5-O-demethylnobiletin and so on， while the metabolites with significant down-regulation included alternariol， fortunellin， etc. A total of 379 differential metabolites were screened between the F2 and F3， with 150 up-regulated and 229 down-regulated， the significant up-regulated metabolites were isoimperatorin， helianyl octanoate and so on， and the significant down-regulated metabolites were hovenoside I， goyasaponin Ⅲ， etc. KEGG pathway enrichment analysis showed that tyrosine metabolism， isoquinoline alkaloid biosynthesis， phenylalanine， tyrosine and tryptophan biosynthesis， tryptophan metabolism， valine， leucine and isoleucine biosynthesis， pantothenate and coenzyme A biosynthesis may be the key pathways affecting metabolite differences of B. balsamifera after sweating treatment. ConclusionSweating can reduce the content of endophytic mycotoxins in B. balsamifera and has a great impact on the synthesis and metabolic pathways of total flavonoids and auxin. This study can provide a reference for the process research on the sweating conditions of B. balsamifera.

Hepatocellular carcinoma(HCC)expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming.Aldolase A(ALDOA)plays a prominent role in glycolysis;however,little is known about its role in HCC development.In the present study,we aim to explore how ALDOA is involved in HCC proliferation.HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout,which is consistent with ALDOA overexpression encouraging HCC prolifera-tion.Mechanistically,ALDOA knockout partially limits the glycolytic flux in HCC cells.Meanwhile,ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase;ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function.A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun,and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells.In HCC patients,the expression level of ALDOA was correlated with the phosphorylation level of c-Jun(Thr93)and poor prognosis.Remarkably,hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models,and the knockdown of Aldoa strikingly decreased HCC development in vivo.Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription,opening additional avenues for anti-cancer therapies.

Near infrared spectroscopy(NIRS)analysis technology has become an important process analysis tool in industrial and agricultural production,and has been widely used for qualitative and quantitative analysis in the fields of tobacco,agriculture,and pharmaceuticals.To address issues such as poor generalization ability and low prediction accuracy in NIRS modeling,a two-dimensional convolutional neural network(2DCNN)quantitative analysis model based on competitive adaptive reweighted sampling(CARS)and Gramian angular difference field(GADF)(CARS-GADF-2DCNN)was proposed.CARS-GADF-2DCNN used the CARS method to select an optimal wavelength set from the full spectrum,then employed GADF to encode the selection results into two-dimensional images,and finally used 2DCNN for prediction analysis.The 2DCNN model consisted of convolutional layers,parallel convolution modules,flattening layer,and fully connected layers.Simulation experiments were conducted on three public near-infrared(NIR)spectral datasets encompassing soil,tablet,and grain datasets to evaluate the CARS-GADF-2DCNN model.The results demonstrated that,compared to the one-dimensional convolutional neural network(1DCNN),the GADF-2DCNN model achieved 16.74％,23.40％,and 7.13％improvement in prediction accuracy for the soil,tablet,and grain datasets,respectively.Compared to GADF-2DCNN,VCPA-GADF-2DCNN,and IRIV-GADF-2DCNN models,the CARS-GADF-2DCNN model further improved prediction accuracy.For the soil dataset,prediction accuracy improved by 39.00％,30.78％and 4.13％;for the tablet dataset,the improvements were 9.52％,6.94％and 2.56％;for the grain dataset,the improvements were 20.57％,9.85％and 15.66％.In conclusion,CARS-GADF-2DCNN effectively selected the optimal wavelength subset from near infrared spectra,and revealed the latent features between different wavelengths.CARS-GADF-2DCNN addresses the issues of high complexity in prediction models and low prediction accuracy in near infrared spectral modeling,and could be effectively applied to near infrared spectral prediction analysis of different substances.

Hepatocellular carcinoma (HCC) expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming. Aldolase A (ALDOA) plays a prominent role in glycolysis; however, little is known about its role in HCC development. In the present study, we aim to explore how ALDOA is involved in HCC proliferation. HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout, which is consistent with ALDOA overexpression encouraging HCC proliferation. Mechanistically, ALDOA knockout partially limits the glycolytic flux in HCC cells. Meanwhile, ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase; ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function. A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun, and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells. In HCC patients, the expression level of ALDOA was correlated with the phosphorylation level of c-Jun (Thr93) and poor prognosis. Remarkably, hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models, and the knockdown of A ldoa strikingly decreased HCC development in vivo. Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription, opening additional avenues for anti-cancer therapies.

This study analyzed the epidemiological characteristics and trends of respiratory syncytial virus (RSV) infections among inpatients with acute respiratory infections (ARI) in a children′s hospital in Shenzhen City inpatients from 2020 to 2023. From January 2020 to December 2023, multiple reverse transcription polymerase chain reaction (RT-PCR) combined with capillary electrophoresis fragment analysis technology was used to detect the nucleic acids of 12 respiratory pathogens, including RSV, in hospitalized children diagnosed with ARI. The patients were divided into six age groups: 0 to <6 months, 6 months to <1 year, 1 to <2 years, 2 to <5 years, 5 to <10 years, and 10 to <18 years. A total of 53 033 children were tested, including 6 830 RSV positive cases, with an overall positivity rate of 12.88%. The annual RSV positivity rates from 2020 to 2023 were 20.04%, 16.18%, 4.89%, and 13.33%, respectively, with statistically significant differences between the years ( χ2=1 185.994, P<0.001). The positive rate of RSV detection decreased with increasing age across all years (all P trend<0.05). From 2020 to 2023, the proportion of RSV-positive cases aged 2 to 5 years and older showed an increasing trend ( P trend<0.001 for all years). Compared to 2023, the median age of RSV-infected children was lower in 2020 ( Z=7.826, P<0.001) and 2021 ( Z=6.106, P<0.001). The proportion of severe infections requiring ICU admission did not change significantly across all years ( χ2=0.179, P=0.981). The RSV epidemic season in 2020 mainly occurred during 28-43 weeks, and in 2021, it spanned from 22-43 weeks. However, in 2022, the season was delayed until the 49th week and lasted for three weeks. In 2023, the seasonal epidemic appeared earlier, starting in the 14th week and lasting for 28 weeks. From 2020 to 2023, the rate of RSV co-infections with other pathogens (mycoplasma pneumoniae, human parainfluenza virus, human bocavirus, human coronavirus, human metapneumovirus, and influenza A) significantly increased (all P trend<0.01). In conclusion, the epidemiological characteristics of RSV infections in Shenzhen Children′s Hospital changed from 2020 to 2023. In 2022, there were only delayed, low-intensity and short-lived seasonal epidemics. However, in 2023, there was an earlier and prolonged epidemic, with increased infections in children aged 2 to 5 years and older and a rise in co-infections, while the proportion of severe infections requiring ICU admission remained unchanged.

Objective A scoping review of domestically and internationally published studies on finite element analysis(FEA)based on three-dimensional(3D)human body model in preventing pressure ulcer(PU)was conducted,aiming to provide new directions for improving the prevention strategies of pressure ulcer.Methods We conducted a systematic search in both Chinese and English medical databases,including PubMed,Embase,CINAHL,Web of Science,Cochrane Library,Sinomed,CNKI,Wanfang and VIP,and engineering database(Engineering Village Compen-dex).The search period was from the inception of each database to July 29,2024.The information was extracted,and the results were analyzed and standardized for reporting.Results A total of 30 studies were included.The general methods of FEA based on 3D human body model include establishing geometric model,meshing,defining material properties,loading and setting boundary conditions,and solving equations.According to the contents of studies,they could be categorized into 4 distinct application domains,including identification of risk groups(n=9),position management(n=9),preventive dressings(n=10)and supportive surfaces(n=7).Conclusion FEA based on 3D human body model provides a foundation to PU biomechanical mechanism research and a scientific basis to the supplement and optimization of clinical prevention.Future studies should integrate clinical problems with simulations and further optimize simulations techniques and protocols.