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.

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.

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.

In recent years, nucleic acid therapy, as a revolutionary therapeutic tool, has shown great potential in the treatment of genetic diseases, infectious diseases and cancer. Lipid nanoparticles (LNPs) are currently the most advanced mRNA delivery carriers, and their emergence is an important reason for the rapid approval and use of COVID-19 mRNA vaccines and the development of mRNA therapy. Currently, mRNA therapeutics using LNP as a carrier have been widely used in protein replacement therapy, vaccines and gene editing. Conventional LNP is composed of four components: ionizable lipids, phospholipids, cholesterol, and polyethylene glycol (PEG) lipids, which can effectively load mRNA to improve the stability of mRNA and promote the delivery of mRNA to the cytoplasm. However, in the face of the complexity and diversity of clinical diseases, the structure, properties and functions of existing LNPs are too homogeneous, and the lack of targeted delivery capability may result in the risk of off-targeting. LNPs are flexibly designed and structurally stable vectors, and the adjustment of the types or proportions of their components can give them additional functions without affecting the ability of LNPs to deliver mRNAs. For example, by replacing and optimizing the basic components of LNP, introducing a fifth component, and modifying its surface, LNP can be made to have more precise targeting ability to reduce the side effects caused by treatment, or be given additional functions to synergistically enhance the efficacy of mRNA therapy to respond to the clinical demand for nucleic acid therapy. It is also possible to further improve the efficiency of LNP delivery of mRNA through machine learning-assisted LNP iteration. This review can provide a reference method for the rational design of engineered lipid nanoparticles delivering mRNA to treat diseases.

Objective Recombinase-aided polymerase chain reaction(RAP)is a sensitive,single-tube,two-stage nucleic acid amplification method.This study aimed to develop an assay that can be used for the early diagnosis of three types of bacteremia caused by Staphylococcus aureus(SA),Pseudomonas aeruginosa(PA),and Acinetobacter baumannii(AB)in the bloodstream based on recombinant human mannan-binding lectin protein(M1 protein)-conjugated magnetic bead(M1 bead)enrichment of pathogens combined with RAP. Methods Recombinant plasmids were used to evaluate the assay sensitivity.Common blood influenza bacteria were used for the specific detection.Simulated and clinical plasma samples were enriched with M1 beads and then subjected to multiple recombinase-aided PCR(M-RAP)and quantitative PCR(qPCR)assays.Kappa analysis was used to evaluate the consistency between the two assays. Results The M-RAP method had sensitivity rates of 1,10,and 1 copies/μL for the detection of SA,PA,and AB plasmids,respectively,without cross-reaction to other bacterial species.The M-RAP assay obtained results for<10 CFU/mL pathogens in the blood within 4 h,with higher sensitivity than qPCR.M-RAP and qPCR for SA,PA,and AB yielded Kappa values of 0.839,0.815,and 0.856,respectively(P<0.05). Conclusion An M-RAP assay for SA,PA,and AB in blood samples utilizing M1 bead enrichment has been developed and can be potentially used for the early detection of bacteremia.

Objective To establish age estimation models of northern Chinese Han adults using cranial suture images obtained by CT and multiplanar reformation(MPR),and to explore the applicability of cranial suture closure rule in age estimation of northern Chinese Han population.Methods The head CT samples of 132 northern Chinese Han adults aged 29-80 years were retrospectively collected.Volume reconstruction(VR)and MPR were performed on the skull,and 160 cranial suture tomography images were generated for each sample.Then the MPR images of cranial sutures were scored according to the closure grading criteria,and the mean closure grades of sagittal suture,coronal sutures(both left and right)and lambdoid sutures(both left and right)were calculated respectively.Finally taking the above grades as independent variables,the linear regression model and four machine learning models for age estimation(gradient boosting regression,support vector regression,decision tree regression and Bayesian ridge regression)were established for northern Chinese Han adults age estimation.The accu-racy of each model was evaluated.Results Each cranial suture closure grade was positively correlated with age and the correlation of sagittal suture was the highest.All four machine learning models had higher age estimation accuracy than linear regression model.The support vector regression model had the highest accuracy among the machine learning models with a mean absolute error of 9.542 years.Conclusion The combination of skull CT-MPR and machine learning model can be used for age esti-mation in northern Chinese Han adults,but it is still necessary to combine with other adult age estima-tion indicators in forensic practice.

Objective To provide anatomical,radiological,and clinical diagnostic and therapeutic references for ankylosing spondylitis and spinal surgical operations.Methods Non-measurement spinal observations,X-ray examinations,and measurements were performed on two spinal specimens,along with digital image acquisition and processing.Results The first specimen included thoracic vertebra 7(T7)to lumbar vertebra 3(L3),with an average total length of 29.7 cm;the second specimen ranged from cervical vertebra 7(C7)to lumbar vertebra 2(L2),with an average total length of 38.3 cm.The specimens showed partial or complete calcification of ligaments,ossification of the small joints and intervertebral discs,and osteoporosis;The anterior-posterior diameter(width)of the vertebral foramen was narrower than that of a normal adult,while most of the superior-inferior diameter(height)was wider.Radiographically,the anterior longitudinal ligament calcification appeared as dot-like or striated,but it was actually flaky in the actual specimens.The specimens provided views of the facet joints,costovertebral joints,and intervertebral foramina that was difficult to demonstrate on two-dimensional X-ray images.Conclusion As ankylosing spondylitis progresses,the range of motion in spinal bending and rotation decreases,as does the extent of thoracic expansion,thereby affecting respiration and complicating procedures such as intraspinal anesthesia and sacral canal injections.In terms of diagnosis,bone specimens and X-ray films allow us to understand the development process and severity of ankylosing spondylitis more directly and accurately.

Purpose::Vibrio vulnificus ( V. Vulnificus) infection is characterized by rapid onset, aggressive progression, and challenging treatment. Bacterial resistance poses a significant challenge for clinical anti-infection treatment and is thus the subject of research. Enhancing host infection tolerance represents a novel infection prevention strategy to improve patient survival. Our team initially identified cytochrome P4501A1 (CYP1A1) as an important target owing to its negative modulation of the body's infection tolerance. This study explored the superior effects of the CYP1A1 inhibitor bergamottin compared to antibiotic combination therapy on the survival of mice infected with multidrug-resistant V. Vulnificus and the protection of their vital organs. Methods::An increasing concentration gradient method was used to induce multidrug-resistant V. Vulnificus development. We established a lethal infection model in C57BL/6J male mice and evaluated the effect of bergamottin on mouse survival. A mild infection model was established in C57BL/6J male mice, and the serum levels of creatinine, urea nitrogen, aspartate aminotransferase, and alanine aminotransferase were determined using enzyme-linked immunosorbent assay to evaluate the effect of bergamottin on liver and kidney function. The morphological changes induced in the presence of bergamottin in mouse organs were evaluated by hematoxylin and eosin staining of liver and kidney tissues. The bacterial growth curve and organ load determination were used to evaluate whether bergamottin has a direct antibacterial effect on multidrug-resistant V. Vulnificus. Quantification of inflammatory factors in serum by enzyme-linked immunosorbent assay and the expression levels of inflammatory factors in liver and kidney tissues by real-time quantitative polymerase chain reaction were performed to evaluate the effect of bergamottin on inflammatory factor levels. Western blot analysis of IκBα, phosphorylated IκBα, p65, and phosphorylated p65 protein expression in liver and kidney tissues and in human hepatocellular carcinomas-2 and human kidney-2 cell lines was used to evaluate the effect of bergamottin on the nuclear factor kappa-B signaling pathway. One-way ANOVA and Kaplan-Meier analysis were used for statistical analysis. Results::In mice infected with multidrug-resistant V. Vulnificus, bergamottin prolonged survival ( p = 0.014), reduced the serum creatinine ( p = 0.002), urea nitrogen ( p = 0.030), aspartate aminotransferase ( p = 0.029), and alanine aminotransferase ( p = 0.003) levels, and protected the cellular morphology of liver and kidney tissues. Bergamottin inhibited interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α expression in serum (IL-1β: p = 0.010, IL-6: p = 0.029, TNF-α: p = 0.025) and inhibited the protein expression of the inflammatory factors IL-1β, IL-6, TNF-α in liver (IL-1β: p = 0.010, IL-6: p = 0.011, TNF-α: p = 0.037) and kidney (IL-1β: p = 0.016, IL-6: p = 0.011, TNF-α: p = 0.008) tissues. Bergamottin did not affect the proliferation of multidrug-resistant V. Vulnificus or the bacterial load in the mouse peritoneal lavage fluid ( p = 0.225), liver ( p = 0.186), or kidney ( p = 0.637). Conclusion::Bergamottin enhances the tolerance of mice to multidrug-resistant V. Vulnificus infection. This study can serve as a reference and guide the development of novel clinical treatment strategies for V. Vulnificus.