OBJECTIVE: Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection frequently develop central nervous system damage, yet the mechanisms driving this pathology remain unclear. This study investigated the primary pathways and key factors underlying brain tissue damage induced by the SARS-CoV-2 beta variant (lineage B.1.351). METHODS: K18-hACE2 and C57BL/6 mice were intranasally infected with the SARS-CoV-2 beta variant. Viral replication, pathological phenotypes, and brain transcriptomes were analyzed. Gene Ontology (GO) analysis was performed to identify altered pathways. Expression changes of host genes were verified using reverse transcription-quantitative polymerase chain reaction and Western blot. RESULTS: Pathological alterations were observed in the lungs of both mouse strains. However, only K18-hACE2 mice exhibited elevated viral RNA loads and infectious titers in the brain at 3 days post-infection, accompanied by neuropathological injury and weight loss. GO analysis of infected K18-hACE2 brain tissue revealed significant dysregulation of genes associated with innate immunity and antiviral defense responses, including type I interferons, pro-inflammatory cytokines, Toll-like receptor signaling components, and interferon-stimulated genes. Neuroinflammation was evident, alongside activation of apoptotic and pyroptotic pathways. Furthermore, altered neural cell marker expression suggested viral-induced neuroglial activation, resulting in caspase 4 and lipocalin 2 release and disruption of neuronal molecular networks. CONCLUSION These findings elucidate mechanisms of neuropathogenicity associated with the SARS-CoV-2 beta variant and highlight therapeutic targets to mitigate COVID-19-related neurological dysfunction.
Animals ; COVID-19/genetics* ; Mice ; Brain/metabolism* ; Apoptosis ; Mice, Inbred C57BL ; SARS-CoV-2/physiology* ; Pyroptosis ; Gene Expression Profiling ; Transcriptome ; Male ; Female

Parameters of peripheral blood cell have been shown as the potential predictors of erectile dysfunction (ED). To investigate the clinical significance of hematological parameters for predicting the risk of rapid ejaculation, we established a rat copulatory model on the basis of ejaculation distribution theory. Blood samples from different ejaculatory groups were collected for peripheral blood cell counts and serum serotonin (5-HT) tests. Meanwhile, the relationship between hematological parameters and ejaculatory behaviors was assessed. Final analysis included 11 rapid ejaculators, 10 normal ejaculators, and 10 sluggish ejaculators whose complete data were available. The platelet (PLT) count in rapid ejaculators was significantly lower than that in normal and sluggish ejaculators, whereas the platelet distribution width (PDW) and mean platelet volume (MPV) were significantly greater in rapid ejaculators. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis showed that the PLT was an independent protective factor for rapid ejaculation. Meanwhile, rapid ejaculators were found to have the lowest serum 5-HT compared to normal and sluggish ejaculators ( P < 0.001). Furthermore, there was a positive correlation between the PLT and serum 5-HT ( r = 0.662, P < 0.001), indicating that the PLT could indirectly reflect the serum 5-HT concentration. In addition, we assessed the association between the PLT and ejaculatory parameters. There was a negative correlation between ejaculation frequency (EF) and the PLT ( r = -0.595, P < 0.001), whereas there was a positive correlation between ejaculation latency (EL) and the PLT ( r = 0.740, P < 0.001). This study indicated that the PLT might be a useful and convenient diagnostic marker for predicting the risk of rapid ejaculation.
Male ; Animals ; Ejaculation/physiology* ; Rats ; Platelet Count ; Pilot Projects ; Serotonin/blood* ; Biomarkers/blood* ; Mean Platelet Volume ; Rats, Sprague-Dawley ; ROC Curve ; Erectile Dysfunction/physiopathology*

Objective To develop a preoperative question prompt list(QPL)for older patients with benign prostatic hyperplasia(BPH)and evaluate its effectiveness in application.Methods This trial adopted a randomized controlled design.The QPL was developed by literature review,expert discussions,and Delphi consultation.Convenience sampling was used to subject 76 older BPH inpatients treated in our department,and then they were randomly divided into control(routine communication,n=38)and intervention(QPL-assisted communication,n=38)groups.Number of the questions patient asking,communication duration,information recall,and communication quality were compared between the 2 groups.Results In the 2 rounds of expert consultation,the response rate of questionnaire was 94.44%and 100%,the authority coefficient was 0.89 and 0.93,the coefficient of variation was 0.05～0.22 and 0～0.11,and Kendall's coefficients was 0.645(Chi-square=87.782,P<0.001)and 0.733(Chi-square=74.789,P<0.001),respectively.The final QPL included 3 themes and 7 questions.The intervention group asked more questions(4.03±1.89 vs 2.11±1.27,P<0.05)but spent similar time for communication(8.18±2.11 vs 7.67±1.72 min,P>0.05).At 1 d before discharge,better information recall(8.74±1.12 vs 6.49±1.68,P<0.001)and communication quality(60.06±6.25 vs 54.86±7.98,P<0.05)were observed in the intervention group when compared with the control group.Conclusion Our developed preoperative communication QPL is of scientificalness and effectiveness for elderly BPH patients.This tool can not only encourage question-asking behavior,but also improve information recall and communication quality in the patients.

Aim To systematically investigate the ac-tive components,targets,and regulatory pathways of Po-lygonum capitatum in intervening atherosclerosis(AS)through network pharmacology,molecular docking and animal experiments.Methods Active components of Polygonum capitatum and AS-related targets were screened and identified through database searches.Protein-protein interaction(PPI)network analysis was performed using the STRING database,followed by GO and KEGG enrichment analyses via the David plat-form.Molecular docking validation was conducted with AutoDock.An AS model was established in Syrian golden hamsters fed a high-fat diet.Predicted pathways and targets were validated using qPCR,ELISA,and histopathological assessment of aortic and hepatic tis-sues via HE staining.Results Network pharmacology identified 27 potential active components of Polygonum capitatum(primarily flavonoids such as quercetin and luteolin)and 110 drug-disease intersection targets,in-cluding core targets MMP-9,ALB,and AKT1.GO and KEGG analyses enriched 593 and 125 pathways,re-spectively,with the NF-κB inflammatory pathway,TNF signaling pathway and lipid metabolism/atherosclerosis pathways highlighted as key mechanisms.Animal ex-periments demonstrated that Polygonum capitatum im-proved serum lipid profiles(reduced TC,TG,LDL-C)in AS hamsters,suppressed the MMP-9/NF-κB signa-ling pathway(downregulated MMP-9,p65 phosphoryla-tion,TNF-α,and IL-6),and inhibited VSMC synthetic phenotypic transformation(upregulated α-SMA and myocardin)by downregulating MCPIP1.Additionally,Polygonum capitatum ameliorated aortic lesions and he-patic lipid deposition in AS hamsters.Conclusions Polygonum capitatum alleviates AS by synergistically regulating the MMP-9/NF-κB/MCPIP1 axis through flavonoid components,suppressing vascular inflammato-ry cascades and maintaining VSMC contractile pheno-types.This reflects Polygonum capitatum's multi-com-ponent,multi-pathway,and multi-target characteristics in combating AS.

Purpose To investigate the role of secreted protein acidic and rich in cysteine(SPARC)expression in the diagnosis and differential diagnosis of mesothelioma.Methods Immunohistochemical EnVision two-step method was used to detect SPARC expression in 40 cases of mesothelioma,4 cases of well-differentiated mesothelial tumour(WDPMT),40 cases of poorly differentiated squamous cell carcinoma of the lung,40 cases of poorly differentiated ad-enocarcinoma of the lung,20 cases each of low-grade and high-grade serous carcinoma of the ovary.The sensitivity and specificity of SPARC,Calretinin,D2-40,and WT-1 expression in mesothelioma were compared and analyzed.Results SPARC showed diffuse strong positive expression in mesothelioma(37/40 cases),medium positive expression in WDPMT(3/4 cases),and focal weak positive expression in a few cases of poorly differentiated squamous cell carcino-ma of the lung(1/40 cases),poorly differentiated adenocarcinoma of the lung(2/40 cases),low-grade serous carci-noma of the ovary(0/20 cases),and high-grade serous carcinoma of the ovary(1/20 cases).In 40 mesotheliomas,the sensitivity of SPARC was 92.5％,and the specificity of SPARC in control tumors(squamous carcinoma of the lung,adenocarcinoma of the lung,and serous carcinoma of the ovary)was 96.7％.Conclusion SPARC is widely expressed in mesotheliomas,with a sensitivity similar to that of Calretinin,D2-40,and WT-1,but with a much higher specificity than other mesothelial markers.It is of great significance in distinguishing between mesothelioma and pulmo-nary poorly differentiated carcinoma and ovarian serous carcinoma.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To investigate the effect of triply periodic minimal surfaces (TPMS) structure and ceramic volume fraction on the mechanical properties of polymer-infiltrated ceramic network (PICN) composite and reveal its strengthening and toughening mechanism.Methods:In this study, TPMS structures with gyroid (G), primitive (P), diamond (D), and ceramic volume fraction (40%, 55%, 70%, 85%) were designed. Porous zirconia scaffolds were prepared using stereolithography technology, and resin was infiltrated into the scaffolds through a vacuum. Then, the PICN composites were obtained after curing. The bending strength, elastic modulus and fracture toughness of PICN were tested using an electronic universal testing machine, with commercial PICN as the control group. The micromorphology of PICN was observed through stereomicroscope and scanning electron microscope. The cytocompatibility of PICN was verified by using cell counting kit, live/dead cell staining and phalloidin staining.Results:The bending strength values of PICN with different ceramic volume fractions ranged from 82.0 MPa to 376.0 MPa, and they gradually increased as the ceramic volume fraction rised. The elastic modulus values of PICN with different ceramic volume fractions ranged from 12.1 GPa to 56.1 GPa. The fracture toughness values of PICN with different ceramic volume fractions ranged from 1.7 MPa·m 1/2 to 6.5 MPa·m 1/2. The bending strength of 85G PICN reached 306.0 MPa, and it had the highest fracture toughness (6.5 MPa·m 1/2) and an appropriate elastic modulus between that of the control group and that of enamel. Under scanning electron microscopy, it could be observed that the cracks branch and deflect at the interface and eventually terminate within the resin phase. After co-culture with PICN, the survival rate of mouse fibroblasts exceeded 80%, indicating that PICN had no cytotoxicity. Conclusions:The PICN composite with TPMS structure can satisfy the mechanical properties and cytocompatibility of dental prosthesis.

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

This study was aimed at establishing a method of ultra-fast quantitative PCR for Brucella detection.We used an exogenous recombinant plasmid as the internal reference and targeted the T4SS secretion system,an important Brucella viru-lence factor,to design specific primers and probes.The sensitivity,specificity,and repeatability of this method were evaluated,and a standard curve was constructed.The coincidence rate of detection findings with this method versus quantitative PCR was determined.This method markedly decreased the detection time to only 10 minutes.The standard curve demonstrated a good linear relationship(Y=-3.410 7x+38.357,R2=0.998 5)with a low minimum detection limit of 10 copies/μL.The method exhibited good specificity and did not specifically amplify several common clinical bacteria other than Brucella.The de-tection of three concentrations of positive plasmids yielded coefficients of variation(CVs)of 0.20％to 0.91％,thus demonstra-ting the method's excellent repeatability.Furthermore,140 clinical samples were analyzed concurrently with the fluorescence PCR method,which yielded a 100％compliance rate and consistent results.Our findings indicated that the Brucella ultra-fast quantitative PCR was ultrafast;had high sensitivity,high specificity,and good specificity;and can be used for the clinical de-tection of Brucella and emergency investigation of epidemics.Therefore,this method is valuable for the early diagnosis of Bru-cella.

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.