ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

AIM: To analyze the factors affecting non-contact intraocular pressure(IOPNCT)measurements after femtosecond laser-assisted small incision lenticule extraction(SMILE), explore the correlation of IOPNCT with central corneal thickness(CCT)and corneal curvature after SMILE, and construct the corresponding regression model which will provide scientific basis for clinical evaluation of the true IOP of patients after SMILE.METHODS: Data from a retrospective analysis of 107 myopic patients(206 eyes)who underwent SMILE and 107 myopic patients(201 eyes)received femtosecond laser-assisted in situ keratomileusis(FS-LASIK)surgery from June 2023 to May 2024 were examined. IOPNCT, CCT, and corneal curvature before surgery and at 1 and 3 mo were collected. The preoperative and postoperative IOPNCT, CCT and corneal curvature were analyzed by ANOVA and Pearson correlation analysis, and multiple linear regression models were constructed to evaluate the association of postoperative changes of IOPNCT, CCT and corneal curvature.RESULTS: There were significant differences in IOPNCT, CCT, and corneal curvature of both SMILE and FS-LASIK patients(all P<0.001), there was no significant difference between two groups and interaction effects(all P>0.05), and the IOPNCT, CCT and corneal curvature at 1 and 3 mo post-surgery were significantly lower than preoperative(all P<0.05). Pearson correlation analysis showed a positive correlation between IOPNCT and CCT at 1 and 3 mo after SMILE(r=0.261, 0.267, all P<0.001), but no significant correlation with corneal curvature(all P>0.05). Multiple linear regression analysis of IOPNCT with CCT and corneal curvature at 1 mo after SMILE indicated that the regression equation was: Y=3.426+0.019X1-0.058X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistical significant difference in the equation(F=7.654, P=0.001); the regression equation for 3 mo after surgery was: Y=2.056+0.020X1-0.038 X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistically significance in the equation(F=7.903, P<0.001). The regression equation of postoperative IOPNCT change(△IOPNCT)and intraoperative cutting corneal thickness(△CCT)and corneal curvature at 1 mo was Y=-2.252+0.008X1+0.587X2(Y represents △IOPNCT, X1 stands for the △CCT, X2 represents the corneal curvature change value), with statistical significant difference in the equation(F=17.550, P<0.001); the regression equation for 3 mo after surgery was: Y=-2.168+0.024X1+0.281X2(Y represents △IOPNCT, X1 represents △CCT, X2 indicates the corneal curvature change values), with statistical significant difference in the equation(F=16.030, P<0.001).CONCLUSION: After SMILE and FS-LASIK surgery, the IOPNCT value of patients was mainly affected by CCT compared with preoperative surgery, and the short-term use of hormone eye drops, fluorometholone, did not cause a significant increase in IOP; both the IOP correction formula at 1 and 3 mo postoperatively can be used clinically to evaluate and correct actual IOP in patients after SMILE.

AIM: To analyze the factors affecting non-contact intraocular pressure(IOPNCT)measurements after femtosecond laser-assisted small incision lenticule extraction(SMILE), explore the correlation of IOPNCT with central corneal thickness(CCT)and corneal curvature after SMILE, and construct the corresponding regression model which will provide scientific basis for clinical evaluation of the true IOP of patients after SMILE.METHODS: Data from a retrospective analysis of 107 myopic patients(206 eyes)who underwent SMILE and 107 myopic patients(201 eyes)received femtosecond laser-assisted in situ keratomileusis(FS-LASIK)surgery from June 2023 to May 2024 were examined. IOPNCT, CCT, and corneal curvature before surgery and at 1 and 3 mo were collected. The preoperative and postoperative IOPNCT, CCT and corneal curvature were analyzed by ANOVA and Pearson correlation analysis, and multiple linear regression models were constructed to evaluate the association of postoperative changes of IOPNCT, CCT and corneal curvature.RESULTS: There were significant differences in IOPNCT, CCT, and corneal curvature of both SMILE and FS-LASIK patients(all P<0.001), there was no significant difference between two groups and interaction effects(all P>0.05), and the IOPNCT, CCT and corneal curvature at 1 and 3 mo post-surgery were significantly lower than preoperative(all P<0.05). Pearson correlation analysis showed a positive correlation between IOPNCT and CCT at 1 and 3 mo after SMILE(r=0.261, 0.267, all P<0.001), but no significant correlation with corneal curvature(all P>0.05). Multiple linear regression analysis of IOPNCT with CCT and corneal curvature at 1 mo after SMILE indicated that the regression equation was: Y=3.426+0.019X1-0.058X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistical significant difference in the equation(F=7.654, P=0.001); the regression equation for 3 mo after surgery was: Y=2.056+0.020X1-0.038 X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistically significance in the equation(F=7.903, P<0.001). The regression equation of postoperative IOPNCT change(△IOPNCT)and intraoperative cutting corneal thickness(△CCT)and corneal curvature at 1 mo was Y=-2.252+0.008X1+0.587X2(Y represents △IOPNCT, X1 stands for the △CCT, X2 represents the corneal curvature change value), with statistical significant difference in the equation(F=17.550, P<0.001); the regression equation for 3 mo after surgery was: Y=-2.168+0.024X1+0.281X2(Y represents △IOPNCT, X1 represents △CCT, X2 indicates the corneal curvature change values), with statistical significant difference in the equation(F=16.030, P<0.001).CONCLUSION: After SMILE and FS-LASIK surgery, the IOPNCT value of patients was mainly affected by CCT compared with preoperative surgery, and the short-term use of hormone eye drops, fluorometholone, did not cause a significant increase in IOP; both the IOP correction formula at 1 and 3 mo postoperatively can be used clinically to evaluate and correct actual IOP in patients after SMILE.

Collagen is a major structural protein in the matrix of animal cells and the most widely distributed and abundant functional protein in mammals. Collagen’s good biocompatibility, biodegradability and biological activity make it a very valuable biomaterial. According to the source of collagen, it can be broadly categorized into two types: one is animal collagen; the other is recombinant collagen. Animal collagen is mainly extracted and purified from animal connective tissues by chemical methods, such as acid, alkali and enzyme methods, etc. Recombinant collagen refers to collagen produced by gene splicing technology, where the amino acid sequence is first designed and improved according to one’s own needs, and the gene sequence of improved recombinant collagen is highly consistent with that of human beings, and then the designed gene sequence is cloned into the appropriate vector, and then transferred to the appropriate expression vector. The designed gene sequence is cloned into a suitable vector, and then transferred to a suitable expression system for full expression, and finally the target protein is obtained by extraction and purification technology. Recombinant collagen has excellent histocompatibility and water solubility, can be directly absorbed by the human body and participate in the construction of collagen, remodeling of the extracellular matrix, cell growth, wound healing and site filling, etc., which has demonstrated significant effects, and has become the focus of the development of modern biomedical materials. This paper firstly elaborates the structure, type, and tissue distribution of human collagen, as well as the associated genetic diseases of different types of collagen, then introduces the specific process of producing animal source collagen and recombinant collagen, explains the advantages of recombinant collagen production method, and then introduces the various systems of expressing recombinant collagen, as well as their advantages and disadvantages, and finally briefly introduces the application of animal collagen, focusing on the use of animal collagen in the development of biopharmaceutical materials. In terms of application, it focuses on the use of animal disease models exploring the application effects of recombinant collagen in wound hemostasis, wound repair, corneal therapy, female pelvic floor dysfunction (FPFD), vaginal atrophy (VA) and vaginal dryness, thin endometritis (TE), chronic endometritis (CE), bone tissue regeneration in vivo, cardiovascular diseases, breast cancer (BC) and anti-aging. The mechanism of action of recombinant collagen in the treatment of FPFD and CE was introduced, and the clinical application and curative effect of recombinant collagen in skin burn, skin wound, dermatitis, acne and menopausal urogenital syndrome (GSM) were summarized. From the exploratory studies and clinical applications, it is evident that recombinant collagen has demonstrated surprising effects in the treatment of all types of diseases, such as reducing inflammation, promoting cell proliferation, migration and adhesion, increasing collagen deposition, and remodeling the extracellular matrix. At the end of the review, the challenges faced by recombinant collagen are summarized: to develop new recombinant collagen types and dosage forms, to explore the mechanism of action of recombinant collagen, and to provide an outlook for the future development and application of recombinant collagen.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.

ObjectivePhotoacoustic pump-probe imaging can effectively eliminate the interference of blood background signal in traditional photoacoustic imaging, and realize the imaging of weak phosphorescence molecules and their triplet lifetimes in deep tissues. However, background differential noise in photoacoustic pump-probe imaging often leads to large fitting results of phosphorescent molecule concentration and triplet lifetime. Therefore, this paper proposes a novel triplet lifetime fitting method for photoacoustic pump-probe imaging. By extracting the phase of the triplet differential signal and the background noise, the fitting bias caused by the background noise can be effectively corrected. MethodsThe advantages and feasibility of the proposed algorithm are verified by numerical simulation, phantom and in vivo experiments, respectively. ResultsIn the numerical simulation, under the condition of noise intensity being 10% of the signal amplitude, the new method can optimize the fitting deviation from 48.5% to about 5%, and has a higher exclusion coefficient (0.88>0.79), which greatly improves the fitting accuracy. The high specificity imaging ability of photoacoustic pump imaging for phosphorescent molecules has been demonstrated by phantom experiments. In vivo experiments have verified the feasibility of the new fitting method proposed in this paper for fitting phosphoometric lifetime to monitor oxygen partial pressure content during photodynamic therapy of tumors in nude mice. ConclusionThis work will play an important role in promoting the application of photoacoustic pump-probe imaging in biomedicine.

ObjectiveTo combine metabolomics， proteomics， and transcriptomics to analyze the biological characteristics of damp-heat toxin accumulation syndrome and damp-heat accumulation syndrome in acute gout. MethodsBlood samples were collected from 15 patients with damp-heat toxin accumulation syndrome and 15 patients with damp-heat accumulation syndrome in acute gout in clinical practice. Metabolomics technology was applied to detect serum metabolites， and an orthogonal partial sample least squares discriminant analysis model was constructed to screen for metabolites with significant intergroup changes， and enrichment pathway analysis and receiver operating characteristic （ROC） curve analysis were performed. Astral data independence acquisition （DIA） was used to detect serum proteins， perform principal component analysis and screen differential proteins， demonstrate differential ploidy by radargram， apply subcellular localisation to analyse protein sources， and finally apply weighted gene co-expression network analysis （WGCNA） to find key proteins. Transcriptome sequencing technology was also applied to detect whole blood mRNA， screen differential genes and perform WGCNA， and construct machine learning models to screen key genes. ResultsMetabolome differential analysis revealed 62 differential metabolites in positive ion mode and 26 in negative ion mode. These differential metabolites were mainly enriched in the mTOR signaling pathway and FoxO signaling pathway， with trans-3,5-dimethoxy-4-hydroxycinnamaldehyde， guanabenz， 4-aminophenyl-1-thio-beta-d-galactopyranoside showing the highest diagnostic efficacy. The proteome differential analysis found that 55 proteins up-regulated and 20 proteins down-regulated in the samples of damp-heat toxin accumulation syndrome. Notably， myelin basic protein （MBP）， transferrin （TF）， DKFZp686N02209， and apolipoprotein B （APOB） showed the most significant differences in expression. Differential proteins were mainly enriched in pathways related to fat digestion and absorption， lipid and atherosclerosis， and cholesterol metabolism. WGCNA showed the highest correlation between damp-heat toxin accumulation syndrome and the brown module， with proteins in this module primarily enriched in the hypoxia-inducible factor 1 （HIF-1） signaling pathway and lipid and atherosclerosis. Transcriptomic differential analysis identified 252 differentially expressed genes， with WGCNA indicating the highest correlation between damp-heat toxin accumulation syndrome and the midnight blue module. The random forest （RF） model was identified as the optimal machine learning model， predicting apolipoprotein B receptor （APOBR）， far upstream element-binding protein 2 （KHSRP）， POU domain class 2 transcription factor 2 （POU2F2）， EH domain-containing protein 1 （EHD1）， and family with sequence similarity 110A （FAM110A） as key genes. Integrated multi-omics analysis suggested that damp-heat toxin accumulation syndrome in the acute phase of gout is closely associated with lipid metabolism， particularly APOB. ConclusionCompared to damp-heat accumulation syndrome in the acute phase of gout， damp-heat toxin accumulation syndrome is more closely associated with lipid metabolism， particularly APOB， and lipid metabolism disorders contribute to the development of damp-heat toxin accumulation syndrome in patients with acute gout.

[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.

High-quality scientific research papers need the support of correct and rigorous statistical methods. However, many papers in transfusion medicine research have problems with the improper use of statistical methods. This paper starts from the transfusion medicine related papers published in China in recent years, summarizes and analyzes common problems in the use of statistics from the aspects of research data type description, statistical method selection, statistical result interpretation and statistical content writing, and discusses possible solutions to provide certain references for the writing of research papers in transfusion medicine.

Background: and Purpose Limited evidence exists on the effectiveness of endovascular treatment (EVT) for acute posterior circulation tandem lesion (PCTL). This study aimed to explore the role of extracranial vertebral artery (VA) stenting in patients with PCTL stroke undergoing EVT. Methods: Individual patient data were pooled from the BASILAR (EVT for Acute Basilar Artery Occlusion Study) and PERSIST (Posterior Circulation Ischemic Stroke) registries. Patients with PCTLs who underwent EVT were included in the present cohort and divided into the stenting and nonstenting groups based on the placement of extracranial VA stents. The primary efficacy outcome was the modified Rankin Scale (mRS) scores at 90 days and 1 year. Safety outcomes included 24-hour symptomatic intracranial hemorrhage (sICH) and all-cause mortality at 90 days and 1 year post-surgery. Results: A combined dataset of 1,320 patients with posterior circulation artery occlusion, including 263 (19.9%) with tandem lesions, of whom 217 (median age, 65 years; 82.9% male) met the inclusion criteria for the analysis. The stenting group had 84 (38.7%) patients, while the non-stenting group had 133 (61.3%). After adjustment for the potential confounders, extracranial VA stenting was associated with favorable shifts in mRS scores at both 90 days (adjusted common odds ratio [OR], 2.30; 95% confidence interval [CI], 1.23–4.28; P<0.01) and 1 year (adjusted OR [aOR], 2.04; 95% CI [1.05–3.97]; P=0.04), along with lower rate of mortality at both 90 days (aOR, 0.45; 95% CI [0.21–0.93]; P=0.01) and 1 year (aOR, 0.36; 95% CI [0.16–0.79]; P=0.01), with no significant difference in sICH incidence (aOR, 0.35; 95% CI [0.06–1.98]; P=0.24). Conclusion Extracranial VA stenting during EVT may improve functional outcomes and reduce mortality in patients with PCTL strokes.