ObjectiveA multiplex amplification system was constructed based on the capillary electrophoresis platform for simultaneous detection of saliva, semen, and vaginal secretions using tissue-specific RNA markers. The aim of this study is to identify the tissue origin of suspicious body fluid stains found at crime scenes and determine whether the body fluid stains at the crime scene are one or several types among saliva, semen, and vaginal secretions. MethodsThirty saliva samples, forty semen samples, and forty vaginal secretion samples (half from 2015 and half from 2024) were collected from healthy adult volunteers. Through primer designing, system formulation, and PCR condition optimization, a multiplex fluorescent amplification system was constructed. The specificity, sensitivity, and detection ability for mixed samples of this system were investigated, and it was tested using real crime scene materials. In the primer design stage, to reduce the requirements for RNA template quality, the amplification products were set within 80-300 bp. In the system formulation stage, dominant and subordinate primers were mainly considered. By reducing the concentration of dominant primers and increasing that of subordinate primers, a capillary electrophoresis spectrum with an appropriate peak height ratio was finally obtained. Additionally, gradient experiments were designed to adjust the concentrations of PCR reagents and PCR amplification conditions, and multiple versions of DNA amplification enzymes were optimized to achieve the best experimental results. ResultsThrough statistical analysis, there was no significant difference in the capillary electrophoresis of the 3 types of body fluid samples from the two years (2015 and 2024), demonstrating that the sample preservation method in this study can preserve samples for a relatively long time. The composite amplification system constructed in this study exhibited high specificity for all 3 types of body fluid, with no cross-reactions between the markers of each type of body fluid. The minimum detection thresholds for the 3 types of body fluid reached 0.002 9, 0.001 5, and 0.42 mg/L, respectively. This system also had a high degree of discrimination for mixed samples, especially for semen-saliva mixtures, where each body fluid marker could still be successfully detected when the concentration ratio of semen to saliva was 100:1. Meanwhile, in the two actual cases presented in this article, the application of this composite amplification system performed outstandingly. ConclusionThe composite amplification detection system constructed in this study can achieve the correct screening of saliva, semen, and vaginal secretions, overcoming the problems such as low specificity and sensitivity of marker tests and unbalanced RFU values of each marker in previous studies. The specificity and sensitivity meet the practical work requirements, and the operation is simple. It provides an analytical and identification method for body fluid stains in actual case and is applicable to the identification of the tissue origin of biological evidence at crime scenes involving sexual assault, indecent assault, and other criminal acts. In the future, more types of body fluid markers will be screened to expand the types of body fluids detected by the system, and body fluid-specific cSNP and cInDel genetic markers will be introduced to infer the sources (individuals and types) of mixed and complex stains more accurately.

OBJECTIVE To assess the scientific rigor， clarity and feasibility of the recommendations of the Guidelines for Evidence-based Use of Biological Agents for the Clinical Treatment of Osteoporosis （hereinafter referred to as the Guideline） through external review， in order to further revise and improve the Guideline recommendations. METHODS This study employed a cross-sectional survey research design， a convenience sampling method was adopted to select frontline medical workers in the field of osteoporosis （including clinical doctors， clinical pharmacists， and nurses） as well as patients or their family members. External review was conducted through a combination of closed-ended and open-ended electronic questionnaires to get feedback from them on the appreciation，clarity and feasibility of the 32 preliminary recommendations in the Guideline. RESULTS A total of 90 external review subjects from 15 hospitals were collected， including 45 clinical doctors， 15 clinical pharmacists， 15 nurses and 15 patients or their family members. The overall appreciation degree of recommendations was 99.38%， the overall clarity degree of recommendations was 98.92%， and the overall feasibility degree of recommendations was 99.65%. At the same time， 111 subjective suggestions were collected， which provided an important reference for the further improvement of the Guideline recommendations. Based on the above feedback， the Guideline steering committee and core expert group revised the wording of 12 draft recommendations without deletion， and finally determined 32 recommendations. CONCLUSIONS The external review provides an important basis for the final formation of the Guideline， further improves the scientific rigor， clarity and feasibility of the recommendations， and ensures the standardization， practicality and implementability of the Guideline.

ObjectiveBola-like short peptides exhibit novel self-assembly properties due to the formation of peptide dimers via hydrogen bonding interactions between their C-terminals. In this configuration, hydrophilic amino acids are distributed at both terminals, making these peptides behave similarly to Bola peptides. The electrostatic repulsive interactions arising from the hydrophilic amino acids at each terminal can be neutralized, thereby greatly promoting the lateral association of β-sheets. Consequently, assemblies with significantly larger widths are typically the dominant nanostructures for Bola-like peptides. To investigate the effect of hydrophilic amino acids on the self-assembly behavior of Bola-like peptides, the peptides Ac-RI₃-CONH₂ and Ac-HI₃-CONH₂ were designed and synthesized using the Bola-like peptide Ac-KI₃-CONH₂ as a template. Their self-assembly behavior was systematically examined. MethodsAtomic force microscopy (AFM) and transmission electron microscopy (TEM) were employed to characterize the morphology and size of the assemblies. The secondary structures of the assemblies were analyzed using circular dichroism (CD) and Fourier transform infrared (FTIR) spectroscopy. Small-angle neutron scattering (SANS) was used to obtain detailed structural information at a short-length scale. Based on these experimental results, the effects of hydrophilic amino acids on the self-assembly behavior of Bola-like short peptides were systematically analyzed, and the underlying formation mechanism was explored. ResultsThe aggregation process primarily involved three steps. First, peptide dimers were formed through hydrogen bonding interactions between their C-terminals. Within these dimers, the hydrophilic amino acids K, R, and H were positioned at both terminals, enabling the peptides to self-assemble in a manner similar to Bola peptides. Next, β-sheets were formed via hydrogen bonding interactions along the peptide backbone. Finally, self-assemblies were generated through the lateral association of β-sheets. The results demonstrated that both Ac-KI₃-CONH₂ and Ac-RI₃-CONH₂ could self-assemble into double-layer nanotubes with diameters of approximately 200 nm. These nanotubes were formed by the edge fusion of helical ribbons, which initially emerged from twisted ribbons. Notably, the primary assemblies of these peptides exhibited opposite chirality: nanofibers formed by Ac-KI₃-CONH₂ displayed left-handed chirality, whereas those formed by Ac-RI₃-CONH₂ exhibited right-handed chirality. This reversal in torsional direction was primarily attributed to the different abilities of K and R to form hydrogen bonds with water. In contrast, Ac-HI₃-CONH₂ formed narrower twisted ribbons with a significantly reduced width of approximately 30 nm, which was attributed to the strong steric hindrance caused by the imidazole rings. The multilayer height of these ribbons was mainly due to the unique structure of the imidazole rings, which can function as both hydrogen bond donors and acceptors, thereby promoting aggregate growth in the vertical direction. ConclusionThe final morphology of the self-assemblies resulted from a delicate balance of various non-covalent interactions. By altering the types of hydrophilic amino acid residues in Bola-like short peptides, the relative strength of non-covalent interactions that drive assembly formation can be effectively regulated, allowing precise control over the morphology and chirality of the assemblies. This study provides a simple and effective approach for constructing diverse self-assemblies and lays a theoretical foundation for the development of functional biomaterials.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: To investigate the association between habitual reading/writing postures and the co-occurrence of common health conditions (overweight/obesity, visual impairment, hypertension, and scoliosis) and comorbidities among children and adolescents, in order to provide data support for the joint prevention of common diseases and comorbidities among children and adolescents. Methods: From September 2021 to June 2022, a multi-stage cluster random sampling method was used to select a total of 4 577 children and adolescents from 16 primary and secondary schools in Ningxia: Jinfeng District of Yinchuan City, Shapotou District of Zhongwei City, Yanchi County of Wuzhong City, and Pingluo County of Shizuishan City. A weighted complex sampling design was used to investigate the association of habitual reading and writing postures with common comorbidities in children and adolescents. Results: The prevalence rates of common diseases among children and adolescents in Ningxia were as follows: overweight/obesity was 22.87%, visual impairment was 62.52%, scoliosis was 2.30%, and hypertension was 1.30%. The prevalence of multimorbidity (co-occurrence of ≥2 conditions) among Ningxia children and adolescents was 15.95%. Multivariate unconditional Logistic regression analysis showed that frequent/always collapsing waist and sitting forward with head lowered increased the risk of common comorbidities in children and adolescents ( OR =1.90, P <0.05). Compared with the corresponding reference group, male children and adolescents aged 9 to 12 years and boys had relatively lower risks of overweight/obesity ( OR =0.71, 0.70); the risk of poor vision among children and adolescents aged 9 to 12 years, male, and urban was relatively low ( OR =0.59, 0.60, 0.73)( P < 0.05 ). Children and adolescents who often/always sat leaning to the left or right were at higher risk of poor vision ( OR =1.78); urban children and adolescents had a higher risk of developing scoliosis ( OR =3.71); children and adolescents aged 9 to 12 had a relatively low risk of developing hypertension ( OR =0.09), and children and adolescents who often/always bent their backs and sat forward on their knees had a higher risk of hypertension ( OR =5.03)( P <0.05). Conclusions Ningxia has a high incidence of common diseases and multiple diseases among children and adolescents, frequent or always collapsing waist and sitting forward with head lowered is associated with common comorbidities in children and adolescents in Ningxia. Proper postural measures for reading and writing should be carried out as soon as possible to encourage children and adolescents to develop good reading and writing habits for effectively preventing and controlling the occurrence of common diseases.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Inflammatory diseases (IDs) are a general term of diseases characterized by chronic inflammation as the primary pathogenetic mechanism, which seriously affect the quality of patient′s life and cause significant social and medical burden. Current drugs for IDs include nonsteroidal anti-inflammatory drugs, corticosteroids, immunomodulators, biologics, and antioxidants, but these drugs may cause gastrointestinal side effects, induce or worsen infections, and cause non-response or intolerance. Given the outstanding performance of metal polyphenol network (MPN) in the fields of drug delivery, biomedical imaging, and catalytic therapy, its application in the diagnosis and treatment of IDs has attracted much attention and significant progress has been made. In this paper, we first provide an overview of the types of IDs and their generating mechanisms, then sort out and summarize the different forms of MPN in recent years, and finally discuss in detail the characteristics of MPN and their latest research progress in the diagnosis and treatment of IDs. This research may provide useful references for scientific research and clinical practice in the related fields.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.