ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Objective To investigate the low-dose CT image denoising and generalization performance of the existing mainstream deep learning based denoising networks.Methods The public AAPM Mayo challenge dataset was used to train the denoising network using 3 image-domain methods(REDCNN,WGAN-VGG,CTformer)and 2 projection-image dual-domain methods(VVBP-UNet,CLEAR),separately.The denoising networks were evaluated quantitatively for peak signal-to-noise ratio(PSNR),structural similarity index,root mean square error,number of network parameters and floating point operations,and their generalization performance was analyzed on the AbdomenCT-1K Dataset.Results Image-domain denoising networks effectively suppressed low-dose CT image noise,with REDCNN demonstrating the best denoising performance and achieving a PSNR of 42.0988 dB.The dual-domain denoising networks were better at preserving tiny tissue structures while removing image noise,with VVBP-UNet performing the best and increasing PSNR to 42.150 9 dB.Conclusion The projection-image dual-domain method exhibits superior denoising and generalization performances than the image-domain method,despite requiring a relatively large amount of network parameters and computations.When computing resources are sufficient,the denoising results obtained by dual-domain method better fulfill the requirements for clinical diagnosis.

Objective:To investigate the distribution of CGG repeat numbers in the FMR1 gene among reproductive-age women in China, providing data reference for carrier screening and genetic counseling of Fragile X syndrome. Methods:This cross-sectional study recruited 16 610 reproductive-age women from 12 medical institutions between July 2022 and October 2023. Peripheral venous blood samples (3 mL) were collected, and genomic DNA was extracted. The number of CGG repeats in the FMR1 gene was determined using the triplet-primed polymerase chain reaction (TP-PCR) combined with capillary electrophoresis technology. Statistical analyses were performed to assess the prevalence and distribution of CGG repeat expansions. Results:Among 16 610 women of childbearing age, 5 684 (34.220%) women had the same number of CGG repeats in the two alleles of FMR1 gene, and 10 926 (65.780%) women had different numbers of repeats in the two alleles. Among the 33 220 FMR1 alleles in 16 610 women of reproductive age, the most common CGG repeat numbers were 29 [48.645% (16 160/33 220)] and 30 [26.276% (8 729/33 220)], while the most frequent CGG genotype was CGG 29/29 [24.726% (4 107/16 610)]. The CGG repeat numbers of FMR1 gene were normal in 16 498 women (99.326%). Among the 112 women (0.674%) with CGG repeat abnormities, 96 (0.578%) women were classified as intermediate carriers, 15 (0.090%) as premutation carriers, and 1 (0.006%) as a full mutation carrier, whose CGG genotype was (36, >200). Conclusion:In the general reproductive-age female population in China, the normal CGG repeat numbers of the FMR1 gene account for 99.326%, while the intermediate carrier rate is 0.578%, and the combined carrier rate of the premutation and full mutation types is 0.096%.

[Objective] To explore the key factors affecting the efficiency and safety of hematopoietic stem cell apheresis. [Methods] The clinical data of 59 healthy donors who underwent allogeneic hematopoietic stem cell donation in the First Affiliated Hospital of Anhui Medical University from January 2021 to June 2024 were retrospectively analyzed. The number of CD34⁺ cells was used to evaluate the eligibility of stem cell collection. The effects of donor gender, age, patient weight, as well as the number of WBC, MNC, RBC, Hb, HCT, PLT, CD34⁺ cells, CD34⁺ percentage and instrument operating parameters on collection efficiency were analyzed. [Results] A total of 59 donors were enrolled, and 68 occasions of stem cell apheresis were performed, with a qualified collection rate of 56%. Donor gender, age, patient weight, total blood circulation volume, anticoagulant dosage, collection time, calcium gluconate dosage and RBC, Hb, HCT levels were not significantly correlated with the collection effect (P>0.05). Multivariate logistic regression analysis showed that the number of MNC cells, CD34⁺ cells and stem cell product volume were the key factors affecting the efficiency and safety. A total of 12 donors had mild adverse reactions during the collection process, and all of them were improved after treatment. [Conclusion] Optimizing apheresis strategy based on the three factors of MNC, WBC count and stem cell product volume on the day of collection will help to achieve high-quality collection and improve the success rate of transplantation.

Objective To investigate the biomechanical differences in plantar pressure,postural stability,and plantar Visual Analogue Scale(VAS)scores between normal feet and unilateral/bilat-eral pes cavus,reveal their unique neuromechanical compensation mechanisms,and construct a sta-bility early warning model based on the minimum center of pressure(COP)trajectory classification.Methods A total of 70 patients with pes cavus from December 2023 to October 2024 were selected as study subjects,including 33 patients in the unilateral pes cavus group and 37 patients in the bilat-eral pes cavus group.During the same period,32 normal feet were included as normal foot group.A flat-panel plantar pressure testing system was used to collect dynamic plantar pressure data and COP trajectories from three groups at a self-selected walking speed.There were no statistically significant differences in baseline data such as age,gender,and body mass index among the three groups(P＞0.05).One-way analysis of variance and the Wilcoxon rank-sum test were used to compare the differences in maximum pressure,contact area,VAS scores,and the 95％confidence ellipse area of the COP among the three groups in 10 plantar regions.Results Patients with pes cavus exhibited lower peak pressure in the MF region compared to normal feet,while higher peak pressure in the M2,M3,and MH regions.Patients with bilateral pes cavus showed lower peak pressure in the T1 region compared to normal feet,and patients with unilateral pes cavus had lower peak pressure in the LH region compared to the normal group(P＜0.05).The plantar contact area in patients with pes cavus was reduced in the T1,M2,M3,M4,MF,and MH regions compared to normal feet(P＜0.05).The 95％confidence ellipse area of the COP was larger in both the bilateral and uni-lateral pes cavus groups compared to the normal foot group(P＜0.001).Unilateral pes cavus pres-ented a specific lateral COP drift(amplitude of 3 to 4 cm),which is a biomechanical manifestation of compensatory eversion of the unaffected foot.Patients with bilateral pes cavus exhibited a"bimod-al oscillation"trajectory(amplitude of 6 to 8 cm),suggesting possible vestibular-spinal regulatory dysfunction and the poorest postural stability.In the pes cavus group,there was a significant in-crease in pressure in the M2,M3,and MH regions,with peak pressures exceeding 190 kPa in pa-tients with bilateral pes cavus,which was highly correlated with plantar pain and could serve as a pain early warning threshold.Conclusion Unilateral and bilateral pes cavus exhibit significantly different neuromechanical compensation patterns.The classification based on the"lateral drift"and"bimodal oscillation"characteristics of the COP trajectory can serve as a stability early warning indi-cator for assessing fall risk.Decompression interventions targeting the key pressure regions of M2,M3,and MH(such as customized orthotic insoles)are the core strategies for alleviating pain and optimizing dynamic gait stability.

AIM To establish the preparation process and quality standard for Zhenggu Pills.METHODS With decoction time,decoction frequency and water addition as influencing factors,comprehensive score for extract yield and transfer rates of epicatechin and naringin as an evaluation index,the decoction process was optimized by orthogonal test.With sugarless paste relative density,medicinal powder fineness,sugarless paste-corn starch ratio,drying temperature and drying time as influencing factors,soft material traits,pill formability,moisture and disintegration time limit as evaluation indices,the formability process was optimized by single factor test.TLC was adopted in the qualitative identification of Dipsaci Radix,salt-processed Psoraleae Fructus,cooked Rhei Radix et Rhizoma and Notoginseng Radix et Rhizoma.HPLC was used for the content determination of paeoniflorin and naringin.RESULTS The optimal decoction process was determined to be 0.5 h for decoction time,two times for decoction frequency,and 10 times for water addition,the comprehensive score was 0.93.The optimal formability process was determined to be 1.21-1.22 for sugarless paste relative density,80 mesh for medicinal powder fineness,1∶0.17-1∶0.18 for sugarless paste-corn starch ratio,70 ℃ for drying temperature,and 24 h for drying time,good soft material traits and pill formability were observable,and moisture and disintegration time limit accored with 2020 edition of Chinese Pharmacopoeia requirements.The TLC spots were clear without negative interference.Two constituents showed good linear relationships within 61.30-490.41 μg/mL(r=0.999 8)and 3.27-26.18 μg/mL(r=0.999 8),whose average recoveries were 100.15％and 98.15％with the RSDs of 0.55％and 2.30％,respectively.CONCLUSION This stable,reliable and specific method can be used for the production and quality evaluation of Zhenggu Pills.

OBJECTIVE To analyze the blood components of Suanzaoren Decoction after oral administration using UHPLC-Q Exactive Orbitrap-MS technology.METHODS Female Sprague-Dawley(SD)rats were used as experimental subjects,and Suanza-oren Decoction was administered orally.Serum samples were collected,and the aqueous extract of Suanzaoren Decoction and the serum were analyzed using UHPLC-Q Exactive Orbitrap-MS technology to identify the prototype components and metabolites absorbed into the blood by comparing and analyzing with the LuMet-TCM database.RESULTS It showed that a total of 458 components were iden-tified in the aqueous extract of Suanzaoren Decoction,and 26 chemical components were identified in the blood,including 23 prototype components and 3 metabolites.CONCLUSION The prototype components absorbed into the blood discovered in this study may be the active ingredients of Suanzaoren Decoction,providing a reference for the research on the pharmacodynamic material basis of Suanza-oren Decoction.

Objective:To investigate the distribution of CGG repeat numbers in the FMR1 gene among reproductive-age women in China, providing data reference for carrier screening and genetic counseling of Fragile X syndrome. Methods:This cross-sectional study recruited 16 610 reproductive-age women from 12 medical institutions between July 2022 and October 2023. Peripheral venous blood samples (3 mL) were collected, and genomic DNA was extracted. The number of CGG repeats in the FMR1 gene was determined using the triplet-primed polymerase chain reaction (TP-PCR) combined with capillary electrophoresis technology. Statistical analyses were performed to assess the prevalence and distribution of CGG repeat expansions. Results:Among 16 610 women of childbearing age, 5 684 (34.220%) women had the same number of CGG repeats in the two alleles of FMR1 gene, and 10 926 (65.780%) women had different numbers of repeats in the two alleles. Among the 33 220 FMR1 alleles in 16 610 women of reproductive age, the most common CGG repeat numbers were 29 [48.645% (16 160/33 220)] and 30 [26.276% (8 729/33 220)], while the most frequent CGG genotype was CGG 29/29 [24.726% (4 107/16 610)]. The CGG repeat numbers of FMR1 gene were normal in 16 498 women (99.326%). Among the 112 women (0.674%) with CGG repeat abnormities, 96 (0.578%) women were classified as intermediate carriers, 15 (0.090%) as premutation carriers, and 1 (0.006%) as a full mutation carrier, whose CGG genotype was (36, >200). Conclusion:In the general reproductive-age female population in China, the normal CGG repeat numbers of the FMR1 gene account for 99.326%, while the intermediate carrier rate is 0.578%, and the combined carrier rate of the premutation and full mutation types is 0.096%.

Objective To evaluate the performance of reconstruction networks with different sparse views under the condition of keeping the same number of model parameters.Methods The number of network channels and network layers were adjusted to make the parameter quantity of each network similar when keeping the structure of each image-domain network and dual-domain network unchanged.The reconstruction performance of each network at different sparsity levels was compared.The AAPM Low-Dose CT Grand Challenge datasets were used in the experiment,including 10 976 images for training,979 images for validation,and 4 256 images for testing.The performance of each model was evaluated visually in combination with objective metrics such as peak signal-to-noise ratio,structural similarity and root mean square error.Results Before adjusting the model parameters,the hybrid domain network Tensor-Net obtained the best visual evaluation and objective evaluation metrics.After parament adjustment,with a similar number of parameters,Tensor-Net outperformed the other models at various projection angles in image anatomical detail recovery,but its structural similarity was slightly lower than that of RED-CNN.The parameters of the hybrid domain model Dual-FBPConvNet were all worse than those of FBPConvNet.Conclusion The hybrid domain model is advantageous in sparse-view CT reconstruction,but it faces more serious overfitting problems.Using a larger image domain model can achieve results similar to those of hybrid domain model.