Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Adolescent idiopathic scoliosis(AIS)is a complex three-dimensional deformity involving coronal,sagittal,and axial planes,with a prevalence that should not be overlooked.With advancements in technology and in-depth research,an increasing number of hospitals and physicians are exploring standardized diagnostic and treatment approaches for AIS.Comprehensive and in-depth understanding is required for AIS,including its etiology,screening and diagnosis,classification,assessment and examination,treatment options,exploration of current focus,and evaluation of quality of life.Such understanding ensures that the diagnostic and treatment are scientific,standardized,and timely.Based on the principles of evidence-based medicine,a consensus on the diagnosis and treatment of AIS is reached after multiple discussions among spinal surgery experts,aiming to provide reference and guidance for clinical practice.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

The plasma matrix is a kind of autologous blood conduct.It has been widely used in maxillofacial tissue regeneration,skin cosmetology and some other fields.Recently,to preserve the dental pulp as well as the teeth,pulp re-generation therapy and apical surgery have become increasingly important as well as the applications of bioactive materi-als.As a kind of autologous bioactive material,the plasma matrix has some natural advantages as it is easy to obtain and malleable.The plasma matrix can be used in the following cases:①pulp revascularization of young permanent teeth with open apical foramina that cannot stimulate apical bleeding;② apical barrier surgery with bone defects and large ar-ea perforation repair with bone defects or root sidewall repair surgery;③ apical surgeries of teeth with large area of api-cal lesions,with or without periodontal diseases.The plasma matrix is a product derived from our blood,and there are no obvious contraindications for its use.Several systematic reviews have shown that the plasma matrix can effectively promote the regenerative repair of dental pulp in patients with periapical diseases.However,the applications of plasma matrix are different because its characteristics are affected by different preparation methods.In addition,there is still a lack of long-term clinical researches on the plasma matrix,and the histological evidences are difficult to obtain,so a large number of in vitro and in vivo experimental studies are still needed.This article will describe the applications of different kinds of plasma matrix for dental pulp regeneration and bone tissue regeneration in apical surgeries to provide references for clinicians in indication selection and prognosis evaluation.

Objective:To investigate the efficacy of arthroscopic superior capsular reconstruction using composite autologous patch graft combined with tenodesis of the long head of the biceps tendon in the treatment of irreparable massive rotator cuff tears (IMRCT).Methods:A retrospective case series study was performed on 11 IMRCT patients who were admitted to Affiliated Fuyang Hospital of Bengbu Medical University (Fuyang People′s Hospital) from May 2020 to June 2022, including 7 males and 4 females, aged 54-74 years [(62.6±7.3)years]. All the patients were treated with arthroscopic superior capsular reconstruction using composite patch graft combined with tenodesis of the long head of the biceps tendon. The Visual Analogue Scale (VAS), Acromiohumeral Distance (AHD), Constant-Murley score and University of California Los Angeles (UCLA) score and active range of motion of the shoulder joint before, at 6 months after surgery and at the last follow-up were compared. At the last follow-up, the integrity of reconstructed superior capsule and the long head of the biceps tendon was evaluated using MRI of the shoulder joint. Postoperative complications were observed.Results:All the patients were followed up for 13-39 months [16(13, 36)months]. The VAS score, AHD, Constant-Murley score, and UCLA score were 2(2, 3)points, (9.1±1.1)mm, (56.1±5.4)points, and (19.7±2.8)points respectively at 6 months after surgery, which were all significantly improved from those before surgery [6(5, 7)points, (5.1±1.2)mm, (37.9±2.2)points, and (11.8±1.2)points] ( P<0.05). The VAS score, AHD, Constant-Murley score, and UCLA score were 0(0, 1)points, (8.4±0.9)mm, (83.6±3.8)points, and (28.2±2.3)points respectively at the last follow-up, which were all significantly improved from those before surgery ( P<0.05). At the last follow-up, the VAS score or AHD were not significantly improved from those at 6 months after surgery ( P>0.05); Constant-Murley score and UCLA score were both significantly improved from those at 6 months after surgery ( P<0.05). At 6 months after surgery, shoulder active ranges of motion in forward flexion, abduction and external rotation were (134.6±13.5)°, (124.6±18.6)° and 45(40, 50)° respectively, which were all significantly improved compared with those before surgery [(63.2±36.1)°, (65.0±23.1)°, and [30(20, 40)°] ( P<0.05). At the last follow-up, shoulder active ranges of motion in forward flexion, abduction and external rotation were (144.1±12.6)°, (139.6±15.4)° and 60(45, 65)° respectively, which were all significantly improved compared with those before surgery ( P<0.05). There were no significant differences in active range of motion of the shoulder in forward flexion, abduction and external rotation between 6 months after surgery and the last follow-up ( P>0.05). At the last follow-up, MRI revealed integrity of the reconstructed superior joint capsule and the long head of the biceps tendon in 10 patients. One patient developed resorption of the greater tuberosity and 1 showed a partial tear of the supraspinatus tendon at 1 year after surgery. Conclusion:Arthroscopic superior capsular reconstruction using composite autologous patch graft combined with tenodesis of the long head of the biceps tendon can relieve shoulder pain, decrease upward displacement of the humerus head, improve the function and range of motion of the shoulder joint, and reduce complications in the treatment of IMRCT.

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges.Conventional strategies always exhibit the insufficiencies in overall coverage,analytical efficiency,and degree of automation,and the results highly rely on the personal knowledge and experience.The goal of this work was to establish a software-aided approach,by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry(UHPLC/IM-QTOF-MS)and in-house high-definition MS2 library,to enhance the identification of prototypes and metabolites of the compound formulae in vivo,taking Sishen formula(SSF)as a template.Seven different MS2 acquisition methods were compared,which demonstrated the potency of a hybrid scan approach(namely high-definition data-independent/data-dependent acquisition(HDDIDDA))in the identification precision,MS1 coverage,and MS2 spectra quality.The HDDIDDA data for 55 reference compounds,four component drugs,and SSF,together with the rat bio-samples(e.g.,plasma,urine,feces,liver,and kidney),were acquired.Based on the UNIFI? platform(Waters),the efficient data processing workflows were estab-lished by combining mass defect filtering(MDF)-induced classification,diagnostic product ions(DPIs),and neutral loss filtering(NLF)-dominated structural confirmation.The high-definition MS2 spectral li-braries,dubbed in vitro-SSF and in vivo-SSF,were elaborated,enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples.Consequently,118 prototypes and 206 metabolites of SSF were identified,with the identification rate reaching 80.51％and 79.61％,respectively.The metabolic pathways mainly involved the oxidation,reduction,hydrolysis,sulfation,methylation,demethylation,acetylation,glucuronidation,and the combined reactions.Conclusively,the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner.

Objective:To construct a random forest prediction model for non-invasive identification of perineural invasion in gallbladder carcinoma (GBC) based on preoperative enhanced CT imaging features.Methods:The clinical data of 180 patients who underwent curative-intent resection for gallbladder carcinoma at the First Affiliated Hospital of Xi′an Jiaotong University from January 2022 to December 2023 were retrospectively analyzed, including 61 males and 119 females with the age of (65.3±10.2) years old. The 180 patients were divided into a training set ( n=126) and a testing set ( n=54), and based on perineural invasion, the 126 patients in the training set were divided into the perineural invasion group ( n=33) and the non-perineural invasion group ( n=93), and the other 54 patients in the testing set, there were 15 patients with perineural invasion and 39 patients without perineural invasion. Clinical data such as gender, age, perineural invasion, carbohydrate antigen 19-9 (CA19-9) level and tumor stage were collected from patients. Multivariate logistic regression model was used to analyze the risk factors of perineural invasion in gallbladder carcinoma patients. The correlation between clinical variables and perineural invasion was ranked in order of importance using the "feature_importance" package in Python software. Then, we developed a random forest prediction model for perineural invasion in gallbladder carcinoma patients, and the area under the receiver operating characteristic (ROC) curve and confusion matrix were used to assess the predictive ability of the model. Results:Multivariate logistic regression model analysis showed that patients with CA19-9 >39.0 U/ml ( OR=5.165, 95% CI: 1.650-16.174), T3 stage ( OR=6.037, 95% CI: 1.571-23.197), T4 stage ( OR=9.996, 95% CI: 2.177-45.898), and lymph node metastasis ( OR=7.829, 95% CI: 2.705-22.627) were with a high risk of perineural invasion occurrence (all P<0.05). The top three variables in the order of the importance ranking were CA19-9, lymph node metastasis, and T stage. Combining the results of multivariate analysis and importance ranking, CA19-9, lymph node metastasis, and T stage were used to develop a random forest prediction model for perineural invasion in gallbladder carcinoma patients. The results of ROC curve analysis showed that the areas under curves of the random forest model in the training and testing sets were 0.8250 and 0.7667, respectively. The confusion matrix results showed that the sensitivity were 75.76% and 73.33%, the specificity were 80.65% and 76.92%, and the accuracy were 79.36% and 75.93% in the training and testing sets, respectively. Conclusion:Random forest prediction model based on preoperative enhanced CT image features can be used as a noninvasive means of identifying perineural invasion in patients with gallbladder carcinoma.

China prospective multicenter birth cohort (Prospective Omics Health Atlas birth cohort, POHA birth cohort) study was officially launched in 2022. This study, in collaboration with 12 participating units, aims to establish a high-quality, multidimensional cohort comprising 20 000 naturally conceived families and assisted reproductive families. The study involves long-term follow-up of parents and offspring, with corresponding biological samples collected at key time points. Through multi-omics testing and analysis, the study aims to conduct multi-omics big data research across the entire maternal and infant life cycle. The goal is to identify new biomarkers for maternal and infant diseases and provide scientific evidence for risk prediction related to maternal diseases and neonatal health.

In this study, 34 deep eutectic solvents (DESs) were successfully prepared for the extraction of proanthocyanidin from Rhodiolae Crenulatae Radix et Rhizomes. The extraction process was optimized using single factor exploration and Box-Behnken design-response surface analysis. The extraction rate was significantly improved when the molar ratio of choline chloride to 1,3-propanediol was 1:3.5 and the water content was 30% (V/V) in DESs. AB-8 macroporous resin and ethyl acetate were used for separation and refining, and the oligomer-rich proanthocyanidin components were eventually obtained. The ultraviolet (UV) and infrared (IR) spectra showed that the proanthocyanidins were mainly composed of catechin and epicatechin. To further clarify the chemical composition of proanthocyanidin, an ion scan list containing 156 proanthocyanidins precursors was obtained by constructing a proanthocyanidins structural library and mass defect filtering (MDF) algorithm, combined with the full mass spectrometry (MS)/dd-MS² scan mode that turns on the "if idle pick others" function. By using ultra-high performance liquid chromatography and high-resolution MS (UHPLC/HRMS), the analysis used both targeted and non-targeted methods to detect proanthocyanidins. Finally, 50 oligomeric proanthocyanidin (OPC) compounds were identified, including 7 monomers, 22 dimers, 20 trimers, and 1 tetramer, most of which were procyanidins of proanthocyanidins (84%), and a small amount of prodelphinidin (14%) and other types of proanthocyanidins (2%), which enabled the systematic characterization of proanthocyanidin components from Rhodiolae Crenulatae Radix et Rhizomes. Meanwhile, the comparison with the grape seeds OPCs standard (United States Pharmacopeia) revealed that the proanthocyanidins in Rhodiolae Crenulatae Radix et Rhizomes were more abundant, suggesting that the proanthocyanidins in Rhodiolae Crenulatae Radix et Rhizomes has promising applications.

In this study,34 deep eutectic solvents(DESs)were successfully prepared for the extraction of proan-thocyanidin from Rhodiolae Crenulatae Radix et Rhizomes.The extraction process was optimized using single factor exploration and Box-Behnken design-response surface analysis.The extraction rate was significantly improved when the molar ratio of choline chloride to 1,3-propanediol was 1:3.5 and the water content was 30％(V/V)in DESs.AB-8 macroporous resin and ethyl acetate were used for separation and refining,and the oligomer-rich proanthocyanidin components were eventually obtained.The ultra-violet(UV)and infrared(IR)spectra showed that the proanthocyanidins were mainly composed of catechin and epicatechin.To further clarify the chemical composition of proanthocyanidin,an ion scan list containing 156 proanthocyanidins precursors was obtained by constructing a proanthocyanidins struc-tural library and mass defect filtering(MDF)algorithm,combined with the full mass spectrometry(MS)/dd-MS2 scan mode that turns on the"if idle pick others"function.By using ultra-high performance liquid chromatography and high-resolution MS(UHPLC/HRMS),the analysis used both targeted and non-targeted methods to detect proanthocyanidins.Finally,50 oligomeric proanthocyanidin(OPC)com-pounds were identified,including 7 monomers,22 dimers,20 trimers,and 1 tetramer,most of which were procyanidins of proanthocyanidins(84％),and a small amount of prodelphinidin(14％)and other types of proanthocyanidins(2％),which enabled the systematic characterization of proanthocyanidin components from Rhodiolae Crenulatae Radix et Rhizomes.Meanwhile,the comparison with the grape seeds OPCs standard(United States Pharmacopeia)revealed that the proanthocyanidins in Rhodiolae Crenulatae Radix et Rhizomes were more abundant,suggesting that the proanthocyanidins in Rhodiolae Crenulatae Radix et Rhizomes has promising applications.