ObjectiveTo compare the differences in color， odor， coumarin content and microbial community composition of Angelicae Dahuricae Radix（ADR） during different drying processes， and to explore the correlation between changes in microbial community composition and changes in quality indexes of ADR. MethodsThe fresh ADR was processed at three drying temperatures（50， 70， 100 ℃） by drying and steaming cutting， semi-fresh cutting and drying， fresh cutting and drying， and sulfur fumigation methods. The color values of samples were extracted by Adobe Photoshop 2022 software and subjected to principal component analysis（PCA）， electronic nose was used to identify the odor information of medicinal powders and subjected to loadings analysis， PCA， and linear discriminant analysis（LDA）， and high performance liquid chromatography（HPLC） was used to determine the contents of five coumarins（bergapten， oxypeucedanin， imperatorin， phellopterin， isoimperatorin）. The samples for microbial detection were taken from fresh dried samples， 50 ℃（dried and steamed cut， sulfur fumigated） samples， and 100 ℃（dried and steamed cut） samples when the water content was 50% and 14%， respectively. And the changes of microbial community composition during processing were determined by high-throughput sequencing method. The relationship between the changes of microbial community composition and the changes of odor， color and active component content of ADR during drying process was analyzed by Pearson correlation analysis. ResultsThe color quantification results showed that an increase in drying temperature led to the decrease of brightness value（L）， and the increases of red-green value（a） and yellow-blue value（b）， and the change of processing method had no obvious effect on the color of medicinal materials. The results of odor quantification showed that W1S， W2S， W5S， W2W and W1W sensor were sensitive to the odor changes of ADR and could be used to distinguish ADR decoction pieces from different processing methods. The results of HPLC showed that the coumarin content of ADR decreased with the increase of drying temperature and the delay of processing time， the optimal processing method was drying and steaming cutting method， and the optimal temperature was 50 ℃. High-throughput sequencing results showed that the dominant bacteria in ADR during processing were Achromobacter， Agrobacterium， Nocardioides， Mycobacterium and Enterobacter， the dominant fungi were Coprinopsis， Meyerozyma and Apiotrichum. The results of correlation analysis showed that the quality indexes of ADR were positively correlated with Agrobacterium， Mycobacterium in bacteria， Candida in fungi， and negatively correlated with Bacillus in bacteria. ConclusionThere are significant differences in the color， odor， coumarin content and microbial community composition of ADR in different drying processes， and the best drying method is drying and steaming cutting at 50 ℃. The relative abundance changes of 9 bacterial genera and 4 fungal genera are closely related to the quality formation of ADR during the drying process.

Objective: To determine the clinical efficacy and mechanism of Piwei Peiyuan Pill (PPP) combined with moxibustion for treating patients with chronic atrophic gastritis (CAG) with spleen and stomach weakness syndrome. Methods: Ninety-six CAG patients with spleen and stomach weakness syndrome who met the inclusion and exclusion criteria were enrolled at the Department of Spleen and Stomach Diseases of the Second Affiliated Hospital of Anhui University of Chinese Medicine from June 2022 to December 2023. The patients were randomly divided into a control, a Chinese medicine, and a combined group using a random number table method, with 32 cases in each group (two cases per group were excluded). The control group was treated with rabeprazole combined with folic acid tablets (both thrice daily), the Chinese medicine group was treated with PPP (8 g, thrice daily), and the combined group was treated with moxa stick moxibustion (once daily) on the basis of the Chinese medicine group for 12 consecutive weeks. Gastric mucosa atrophy in the three groups was observed before and after treatment. The gastric mucosal pathological score was evaluated. The Patient Reported Outcome (PRO) scale was used to evaluate the patients′ physical and mental health status and quality of life.An enzyme-linked immunosorbent assay was used to detect serum tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-4, IL-10, IL-37, and transforming growth factor (TGF)-β levels in each group. Real-time fluorescence PCR was used to detect the relative expression levels of signal transducer and activator of transcription 3 (STAT3) and mammalian target of rapamycin (mTOR) mRNA in each group. Western blotting was used to detect the relative expression levels of proteins related to the STAT3/mTOR signaling pathway, and the adverse drug reactions and events were recorded and compared. Results: There was no statistical difference in age, gender, disease duration, family history of gastrointestinal tumors, alcohol consumption history, and body mass index among the three groups of patients.The total therapeutic efficacy rates of the control, Chinese medicine, and combined groups in treating gastric mucosal atrophy were 66.67% (20/30), 86.67% (26/30), and 90.00% (27/30), respectively (P<0.05). Compared to before treatment, the pathological and PRO scale scores of gastric mucosa in each group decreased after treatment, and TNF-α, IL-1β, IL-37, and TGF-β levels decreased. The relative STAT3 and mTOR mRNA expression levels, as well as the relative STAT3, p-STAT3, mTOR, and p-mTOR protein expression levels decreased (P<0.05), whereas the IL-4 and IL-10 levels increased (P<0.05). After treatment, compared to the control group, the pathological score of gastric mucosa, PRO scale score, TNF-α, IL-1β, IL-37, TGF-β content, relative STAT3 and mTOR mRNA expression levels, and relative STAT3, p-STAT3, mTOR, and p-mTOR protein expression levels in the Chinese medicine and combined groups after treatment were reduced (P<0.05), whereas the IL-4 and IL-10 levels increased (P<0.05). After treatment, compared to the Chinese medicine group, the combined group showed a decrease in relative STAT3, mTOR mRNA expression levels, and STAT3, p-STAT3, mTOR, and p-mTOR protein expression levels (P<0.05). Conclusion The combination of PPP and moxibustion may regulate the inflammatory mechanism of the body by inhibiting the abnormal activation of the STAT3/mTOR signaling pathway, upregulating related anti-inflammatory factor levels, downregulating pro-inflammatory factor expression, and increasing related repair factor expression, thereby promoting the recovery of atrophic gastric mucosa, reducing discomfort symptoms, and improving the physical and mental state of CAG patients with spleen and stomach weakness syndrome.

Background: Hearing loss (HL) is one of the most common sensory disorders, affecting over 5-8% of the world's population. Approximately half of HL cases are attributed to genetic factors. In hereditary deafness, about 75-80% is inherited through autosomal recessive inheritance, and common pathogenic genes include GJB2 and SLC26A4. Pathogenic variants in the SLC26A4gene are the leading cause of hereditary hearing loss in humans, second only to the GJB2 gene. Variants in the SLC26A4gene cause hearing loss, which can be non-syndromic autosomal recessive deafness (DFNB4, OMIM #600791) associated with enlarged vestibular aqueduct (EVA) or Pendred syndrome (Pendred, OMIM #605646). DFNB4 is characterized by sensorineural hearing loss combined with EVA or less common cochlear malformation defect. Pendred syndrome is characterized by bilateral sensorineural hearing loss with EVA and an iodine defect that can lead to thyroid goiter. Currently, it is known that EVA is associated with variants in the SLC26A4 gene and is a penetrant feature of SLC26A4-related HL. Predominant mutations in these genes differ significantly across populations. For instance, predominant SLC26A4 mutations differ among populations, including p.T416P and c.1001G>A in Caucasians, p.H723R in Japanese and Koreans, and c.919-2A>G in Han Taiwanese and Han Chinese. On the other hand, there has been no study of hearing loss related to SLC26A4 gene variants among Mongolians, which is the basis of our research. Aim: We aimed to identify the characteristics of the SLC26A4 gene variants in Mongolian people with Enlarged vestibular aqueduct and Mondini malformation. Materials and Methods: In 2022-2024, We included 13 people with hearing loss and enlarged vestibular aqueduct, incomplete cochlea (1.5 turns of the cochlea with cystic apex- incomplete partition type II- Mondini malformation) were examined by CT scan of the temporal bone in our study. WES (Whole exome sequencing) analysis was performed in the Genetics genetic-laboratory of the National Taiwan University Hospital. Results: Genetic analysis revealed 26 confirmed pathogenic variants of bi-allelic SLC26A4 gene of 8 different types in 13 cases, and c.919-2A>G variant was dominant with 46% (12/26) in allele frequency, and c.2027T>A (p.L676Q) variant 19% (5/26), c.1318A>T(p.K440X) variant 11% (3/26), c.1229C>T (p.T410M) variant 8% (2/26) ) , c.716T>A (p.V239D), c.281C>T (p.T94I), c.1546dupC, and c.1975G>C (p.V659L) variants were each 4% (1/26)- revealed. Two male children, 11 years old (SLC26A4: c.919-2A>G) and 7 years old (SLC26A4: c.919-2A>G:, SLC26A4: c.2027T>A (p.L676Q))had history of born normal hearing and progressive hearing loss. Conclusions 1. 26 variants of bi-allelic SLC26A4 gene mutation were detected in Mongolian people with EVA and Mondini malformation, and c.919-2A>G was the most dominant allele variant, and rare variants such as c.1546dupC, c.716T>A (p.V239D) were detected. 2. Our study shows that whole-exome sequencing (WES) can identify gene mutations that are not detected by polymerase chain reaction (PCR) or NGS analysis.

By employing the analytic hierarchy process(AHP), the CRITIC method(a weight determination method based on indicator correlations), and the AHP-CRITIC hybrid weighting method, the weight coefficients of evaluation indicators were determined, followed by a comprehensive score comparison. The grey correlation analysis was then performed to analyze the results calculated using the hybrid weighting method. Subsequently, a backpropagation-artificial neural network(BP-ANN) model was constructed to predict the extraction process parameters and optimize the extraction process for Shenxiong Huanglian Jiedu Granules(SHJG). In the extraction process, an L_9(3~4) orthogonal experiment was designed to optimize three factors at three levels, including extraction frequency, water addition amount, and extraction time. The evaluation indicators included geniposide, berberine, ginsenoside Rg_1 + Re, ginsenoside Rb_1, ferulic acid, and extract yield. Finally, the optimal extraction results obtained by the orthogonal experiment, grey correlation analysis, and BP-ANN method were compared, and validation experiments were conducted. The results showed that the optimal extraction process involved two rounds of aqueous extraction, each lasting one hour; the first extraction used ten times the amount of added water, while the second extraction used eight times the amount. In the validation experiments, the average content of each indicator component was higher than the average content obtained in the orthogonal experiment, with a higher comprehensive score. The optimized extraction process parameters were reliable and stable, making them suitable for subsequent preparation process research.
Drugs, Chinese Herbal/analysis* ; Neural Networks, Computer

Several types of arthritis share the common feature that the generation of inflammatory mediators leads to joint cartilage degradation. However, the shared mechanism is largely unknown. H2BK120ub1 was reportedly involved in various inflammatory diseases but its role in the shared mechanism in inflammatory joint conditions remains elusive. The present study demonstrated that levels of cartilage degradation, H2BK120ub1, and its regulator WW domain-containing adapter protein with coiled-coil (WAC) were increased in cartilage in human rheumatoid arthritis (RA) and osteoarthritis (OA) patients as well as in experimental RA and OA mice. By regulating H2BK120ub1 and H3K27me3, WAC regulated the secretion of inflammatory and cartilage-degrading factors. WAC influenced the level of H3K27me3 by regulating nuclear entry of the H3K27 demethylase KDM6B, and acted as a key factor of the crosstalk between H2BK120ub1 and H3K27me3. The cartilage-specific knockout of WAC demonstrated the ability to alleviate cartilage degradation in collagen-induced arthritis (CIA) and collagenase-induced osteoarthritis (CIOA) mice. Through molecular docking and dynamic simulation, doxercalciferol was found to inhibit WAC and the development of cartilage degradation in the CIA and CIOA models. Our study demonstrated that WAC is a key factor of cartilage degradation in arthritis, and targeting WAC by doxercalciferol could be a viable therapeutic strategy for treating cartilage destruction in several types of arthritis.

Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background: Intraoperative hypercapnia reduces the time to emergence from volatile anesthetics, but few clinical studies have explored the effect of hypercapnia on the emergence time from intravenous (IV) anesthesia. We investigated the effect of inducing mild hypercapnia during the recovery period on the emergence time after total IV anesthesia (TIVA). Methods: Adult patients undergoing transurethral lithotripsy under TIVA were randomly allocated to normocapnia group (end-tidal carbon dioxide [ETCO2] 35–40 mmHg) or mild hypercapnia group (ETCO2 50-55 mmHg) during the recovery period. The primary outcome was the extubation time. The spontaneous breathing-onset time, voluntary eye-opening time, and hemodynamic data were collected. Changes in the cerebral blood flow velocity in the middle cerebral artery were assessed using transcranial Doppler ultrasound. Results: In total, 164 patients completed the study. The extubation time was significantly shorter in the mild hypercapnia (13.9 ± 5.9 min, P = 0.024) than in the normocapnia group (16.3 ± 7.6 min). A similar reduction was observed in spontaneous breathing-onset time (P = 0.021) and voluntary eye-opening time (P = 0.008). Multiple linear regression analysis revealed that the adjusted ETCO2 level was a negative predictor of extubation time. Middle cerebral artery blood flow velocity was significantly increased after ETCO2 adjustment for mild hypercapnia, which rapidly returned to baseline, without any adverse reactions, within 20 min after extubation. Conclusions Mild hypercapnia during the recovery period significantly reduces the extubation time after TIVA. Increased ETCO2 levels can potentially enhance rapid recovery from IV anesthesia.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.