ObjectiveTo establish a rapid analytical method for identifying the differential components in Poria cocos medicinal materials based on ultra performance liquid chromatography-quadrupole-electrostatic field orbital trap high-resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， combined with mass defect filtering（MDF） and molecular network integration techniques. MethodsUPLC-Q-Orbitrap-MS was used for MS data acquisition and identification of P. cocos medicinal materials， with the help of MDF for the study of cleavage behavior and structural identification of triterpenoids. According to the similarity of MS/MS fragmentation patterns of each component， global natural product social molecular network（GNPS） was established， and Cytoscape 3.6.1 was used to screen molecular clusters with similar structures and the the structure of main compound classes were identified and confirmed. Multivariate statistical analyses such as principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to screen the differential components of the five P. cocos medicinal materials with the variable importance in the projection（VIP） value>1 and P<0.05 as the criteria. ResultsA total of 66 compounds were identified by database comparison， 8 compounds were newly identified by MDF， 28 compounds were newly identified by GNPS， and a total of 102 chemical compounds were identified， including 43 triterpenoids， 16 saccharides， 26 amino acids and peptides， 3 nucleosides， and 14 other compounds. Triterpenoids were predominant in Poriae Cutis and wild Fushen， amino acids and peptides were the most abundant in Poria and cultivated Fushen， carbohydrates were the most abundant in Poriae Cutis. Type Ⅰ and Ⅱ triterpenoids had higher amounts in Poria and cultivated Fushen， type Ⅲ triterpenoids were more abundant in Poriae Cutis， all four types of triterpenoids were higher in Fushenmu， and type Ⅰ， Ⅱ， and Ⅳ triterpenoids were higher in wild Fushen. A total of 12 common differential chemical constituents were screened， including serine， guanosine， gallic acid， 2-octenal， maltotriose， trametenolic acid， dehydroeburicoic acid， dehydrotrametenolic acid， poricoic acid A， poricoic acid B， poricoic acid E and G， but the relative contents of them varied significantly among different medicinal materials. ConclusionAmong the five P. cocos medicinal materials， the types of constituents are generally similar， but their relative contents differed significantly among these medicinal materials， especially in the distribution of triterpenoids. The integration of UPLC-Q-Orbitrap-MS， MDF and GNPS can provide a reference for the rapid qualitative analysis of other Chinese medicines.

ObjectiveTo establish a rapid analytical method for identifying the differential components in Poria cocos medicinal materials based on ultra performance liquid chromatography-quadrupole-electrostatic field orbital trap high-resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， combined with mass defect filtering（MDF） and molecular network integration techniques. MethodsUPLC-Q-Orbitrap-MS was used for MS data acquisition and identification of P. cocos medicinal materials， with the help of MDF for the study of cleavage behavior and structural identification of triterpenoids. According to the similarity of MS/MS fragmentation patterns of each component， global natural product social molecular network（GNPS） was established， and Cytoscape 3.6.1 was used to screen molecular clusters with similar structures and the the structure of main compound classes were identified and confirmed. Multivariate statistical analyses such as principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to screen the differential components of the five P. cocos medicinal materials with the variable importance in the projection（VIP） value>1 and P<0.05 as the criteria. ResultsA total of 66 compounds were identified by database comparison， 8 compounds were newly identified by MDF， 28 compounds were newly identified by GNPS， and a total of 102 chemical compounds were identified， including 43 triterpenoids， 16 saccharides， 26 amino acids and peptides， 3 nucleosides， and 14 other compounds. Triterpenoids were predominant in Poriae Cutis and wild Fushen， amino acids and peptides were the most abundant in Poria and cultivated Fushen， carbohydrates were the most abundant in Poriae Cutis. Type Ⅰ and Ⅱ triterpenoids had higher amounts in Poria and cultivated Fushen， type Ⅲ triterpenoids were more abundant in Poriae Cutis， all four types of triterpenoids were higher in Fushenmu， and type Ⅰ， Ⅱ， and Ⅳ triterpenoids were higher in wild Fushen. A total of 12 common differential chemical constituents were screened， including serine， guanosine， gallic acid， 2-octenal， maltotriose， trametenolic acid， dehydroeburicoic acid， dehydrotrametenolic acid， poricoic acid A， poricoic acid B， poricoic acid E and G， but the relative contents of them varied significantly among different medicinal materials. ConclusionAmong the five P. cocos medicinal materials， the types of constituents are generally similar， but their relative contents differed significantly among these medicinal materials， especially in the distribution of triterpenoids. The integration of UPLC-Q-Orbitrap-MS， MDF and GNPS can provide a reference for the rapid qualitative analysis of other Chinese medicines.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Hemoglobin, the principal protein in red blood cells, is crucial for oxygen transport in the bloodstream. The quantification of hemoglobin concentration is indispensable in medical diagnostics and health management, which encompass the diagnosis of anemia and the screening of various blood disorders. Immunological methods, based on antigen-antibody interactions, are distinguished by their high sensitivity and accuracy. Consequently, it is necessary to develop hemoglobin-specific antibodies characterized by high specificity and affinity to enhance detection accuracy. In this study, we immunized a Bactrian camel (Camelus bactrianus) with human hemoglobin and subsequently constructed a nanobody library. Utilizing a solid-phase screening method, we selected nanobodies and evaluated the binding activity of the screened nanobodies to hemoglobin. Initially, human hemoglobin was used to immunize a Bactrian camel. Following four immunization sessions, blood was withdrawn from the jugular vein, and a nanobody library with a capacity of 2.85×10⁸ colony forming units (CFU) was generated. Subsequently, ten hemoglobin-specific nanobody sequences were identified through three rounds of adsorption-elution-enrichment assays, and these nanobodies were subjected to eukaryotic expression. Finally, enzyme-linked immunosorbent assay and biolayer interferometry were employed to evaluate the stability, binding activity, and specificity of these nanobodies. The results demonstrated that the nanobodies maintained robust binding activity within the temperature range of 20-40 ℃ and exhibited the highest binding activity at pH 7.0. Furthermore, the nanobodies were capable of tolerating a 10% methanol solution. Notably, among the nanobodies tested, VHH-12 displayed the highest binding activity to hemoglobin, with a half maximal effective concentration (EC₅₀) of 10.63 nmol/L and a equilibrium dissociation constant (K_D) of 2.94×10^-7 mol/L. VHH-12 exhibited no cross-reactivity with a panel of eight proteins, such as ovalbumin and bovine serum albumin, while demonstrating partial cross-reactivity with hemoglobin derived from porcine, goat, rabbit, and bovine sources. In this study, a hemoglobin-specific high-affinity nanobody was successfully isolated, demonstrating potential applications in disease diagnosis and health monitoring.
Animals ; Camelus/immunology* ; Single-Domain Antibodies/immunology* ; Hemoglobins/immunology* ; Humans ; Peptide Library

Objective: To estimate the epidemic threshold and graded intensity thresholds of influenza in Zhejiang Province from 2012 to 2023 using the moving epidemic method (MEM), and evaluate the intensity of influenza epidemics, so as to provide the reference for influenza prevention and control in Zhejiang Province. Methods: The positive rates of influenza virus per week during the influenza epidemic seasons (from 40th week to 20th week of the following year) in Zhejiang Province from 2012 to 2022 were collected through the Chinese Influenza Surveillance Information System. A MEM model was established and optimized using cross-validation. The maximum accumulated rates percentage was used to divide the epidemic into pre-epidemic, epidemic, and post-epidemic periods, and to estimate the epidemic thresholds and graded intensity thresholds. The intensity of influenza epidemics in Zhejiang Province during the 2022-2023 epidemic season were assessed. Results: The positive rates of influenza virus in five epidemic seasons from 2012 to 2022 were included in the model. The MEM model performed best when the parameter δ was set to 1.5, with a sensitivity of 0.971, a specificity of 0.745, and a Youden's index of 0.716. According to the model analysis, the epidemic beginning and ending thresholds of influenza in Zhejiang Province during the 2022-2023 epidemic season were 19.32% and 10.92%, respectively, and the medium, high, and extremely high intensity thresholds were 48.65%, 63.49%, and 68.47%, respectively. During 2022-2023, the influenza epidemic was in the pre-epidemic period from the 40th week in 2022 to the 7th week in 2023; the epidemic period was from the 8th to the 18th week, the epidemic intensity was low in the 8th week and increased to a high level in the 9th week, and reached to a extremely high level from the 10th to the 13th week, then fell to the high and the medium level in the 14th week and 15th week, respectively, and fell to a low level from the 16th to the 18th week; the influenza epidemic entered the post-epidemic period since the 19th week. Conclusion MEM could be applied for evaluation of influenza epidemic intensity, providing the reference for early identification and taking graded preventive and control measures.

Objective To investigate the epidemiological characteristics and mutation spectrum of monogenic diseases in Chinese population through a large-scale,multicenter carrier screening.Methods This study was conducted among a total of 33 104 participants(16 610 females)from 12 clinical centers across China.Carrier status for 223 genes was analyzed using high-throughput sequencing and different PCR methods.Results The overall combined carrier frequency was 55.58%for 197 autosomal genes and 1.84%for 26 X-linked genes in these participants.Among the 16 669 families,874 at-risk couples(5.24%)were identified.Specifically,584 couples(3.50%)were at risk for autosomal genes,306(1.84%)for X-linked genes,and 16 for both autosomal and X-linked genes.The most frequently detected autosomal at-risk genes included GJB2(autosomal recessive deafness type 1A,393 couples),HBA1/HBA2(α-thalassemia,36 couples),PAH(phenylketonuria,14 couples),and SMN1(spinal muscular atrophy,14 couples).The most frequently detected X-linked at-risk genes were G6PD(G6PD deficiency,236 couples),DMD(Duchenne muscular dystrophy,23 couples),and FMR1(fragile X syndrome,17 couples).After excluding GJB2 c.109G>A,the detection rate of at-risk couples was 3.91%(651/16 669),which was lowered to 1.72%(287/16 669)after further excluding G6PD.The theoretical incidence rate of severe monogenic birth defects was approximately 4.35‰(72.5/16 669).Screening for a battery of the top 22 most frequent genes in the at-risk couples could detect over 95%of at-risk couples,while screening for the top 54 genes further increased the detection rate to over 99%.Conclusion This study reveals the carrier frequencies of 223 monogenic genetic disorders in the Chinese population and provides evidence for carrier screening strategy development and panel design tailored to the Chinese population.In carrier testing,genetic counseling for specific genes or gene variants can be challenging,and the couples need to be informed of these difficulties before testing and provided with options for not screening these genes or gene variants.