ObjectiveTo evaluate the detection specificity for clinical samples and the detection capability for standard substances of five commercially available multiplex polymerase chain reaction (PCR) nucleic acid detection kits (hereinafter referred to as the kits) for respiratory pathogens, and to provide a reference for selecting appropriate detection kits for multi-pathogen nucleic acid testing of respiratory infections. MethodsA total of 60 respiratory pathogen-positive clinical samples with known redults were selected and tested using the five kits (labeled as A, B, C, D, and E). The detection rates and Kappa coefficients were calculated to evaluate the consistency between the results from these kits and those from single-pathogen PCR kits. According to the limit of detection (LOD) provided by the kits, standard substances of respiratory pathogens (including 12 types such as influenza virus, Mycoplasma pneumoniae, and Bordetella pertussis) were diluted to four concentrations (250, 500, 1 000, and 2 000 copies·mL⁻¹). All five kits were used for detection to evaluate their respective detection capabilities. ResultsCompared with the results from single-pathogen PCR kits, the five tested kits demonstrated good consistency (all Kappa >0.80). Among them, Kit A had the highest detection rate (100.00%), followed by Kits C and E (98.33%), and then Kits B and D (95.00%). All five kits showed a relatively low false negative rate (FNR) for samples with a cycle threshold (Ct) value ≤35 (≤2.38%). However, for samples with Ct values>35, the FNR increased accordingly(average FNR=6.67%, P=0.029). Kit C exhibited the highest detection sensitivity for the tested standard substances (average LOD: 458.33 copies·mL⁻¹), followed by Kit D, then Kits A/E, and finally Kit B. ConclusionThe five multiplex PCR kits showed good consistency with single-pathogen detection results, but each had its own performance emphasis. Kit A, with the highest detection rate and high throughput, is suitable for targeted viral screening. Kit B, covering the broadest pathogen spectrum (including fungi/bacteria), is suitable for comprehensive respiratory pathogen screening. Kits C, D and E, are applicable for rapid detection. It is important to note that the detection efficacy of all kits decreases for low viral load samples with Ct values >35. In practical application, selection should be based on specific screening objectives, throughput requirements, and sample types.

Background and aims The mechanism of cholestatic liver injury (CLI) is unclear, and effective therapies are lacking. While peroxisome proliferator-activated receptor alpha (PPARα) agonists show potential hepatoprotective effect and pyroptosis is implicated in hepatocellular damage, how PPARα activation mitigates lithocholic acid (LCA)-induced pyroptosis remains unknown. Methods The hepatoprotective effect of PPARα agonists was evaluated in a mouse model of intrahepatic cholestasis induced by LCA. Liver injury was assessed via serum biochemistry, hematoxylin and eosin and TUNEL staining, and electron microscopy. Pyroptosis pathways were analyzed using real-time quantitative polymerase chain reaction, Western blot, and co-immunoprecipitation. Results Combined morphological, histopathological, and biochemical analyses confirmed that PPARα activation protects against CLI. Compared with LCA treatment alone, PPARα activation significantly attenuated the elevation of serum lactate dehydrogenase (LDH), the increased TUNEL-positive cells, and the formation of hepatocyte membrane pores. Mechanistically, PPARα activation suppressed both NOD-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis and apoptosis protease-activating factor-1 (APAF-1)/CASPASE-3/GSDME-mediated pyroptosis. Furthermore, PPARα agonist pretreatment inhibited activation of the nuclear factor-kappa B (NF-κB) and forkhead box O1 (FOXO1) signaling pathways. Conclusions PPARα protects against LCA-induced CLI by inhibiting both NLRP3 inflammasome-mediated pyroptosis associated with NF-κB and APAF-1/CASPASE-3/GSDME-mediated pyroptosis associated with the FOXO1 signaling pathway.

Diabetic erectile dysfunction is a com-mon complication of diabetes that severely affects the quality of life of men and their sexual partners.Active participation in scientific research on diabet-ic erectile dysfunction is particularly important,and animal models are an important basis for exploring the pathogenesis of the disease,evaluating the effi-cacy of drug treatments,and developing new drugs.The pathogenesis of diabetic erectile dys-function is complex,and current treatments mainly focus on regulating blood sugar,anti-oxidative stress,PDE5 inhibitors,stem cell therapy,inhibiting neurovascular injury,anti-fibrosis,traditional Chi-nese medicine,and other aspects.In particular,cor-recting hyperglycemia is crucial for preventing or stopping the progression of the disease.This article summarizes and updates existing treatment meth-ods by reviewing the latest literature,and reviews the animal models used in different treatment methods,in order to provide a reference for animal experiments and clinical treatment.

BACKGROUND:Knee osteoarthritis is a common degenerative disease that significantly impacts patients'quality of life and increases the societal healthcare burden.Early and accurate diagnosis of knee osteoarthritis is crucial for the treatment and prognosis of patients.Traditional diagnostic methods are not only subjective and time-consuming but also do not guarantee consistently high accuracy.OBJECTIVE:To develop an automatic diagnostic method for knee osteoarthritis based on deep learning,utilizing deep learning networks to improve diagnostic accuracy and efficiency.METHODS:A new network model,YOLOV8-ViT,was proposed by replacing the backbone network of YOLOv8n with the Efficient-ViT network and incorporating attention mechanisms for the automatic identification and classification of X-ray images of knee osteoarthritis.The experimental dataset included 5 078 X-ray images of patients with knee osteoarthritis obtained from the Third Affiliated Hospital of Guangzhou University of Chinese Medicine.Three imaging physicians annotated the sites of knee osteoarthritis and classified them according to the Kellgren-Lawrence grading standard using Labelme software,and the results were combined.The evaluation indicators used in this study included Precision,F1 score,mean average precision(mAP),Recall,val/box_loss,val/cls_loss,and val/dfl_loss.RESULTS AND CONCLUSION:The experimental results showed that the YOLOV8-ViT model outperformed the YOLOv5n,YOLOv8n,and YOLOv9n models in terms of precision,mAP50,mAP50-95,F1 score,and Recall,while lowering val/box_loss,val/cls_loss,and val/dfl_loss by 0.496,0.45,and 0.523;1.037,0.305,and 0.728;and 0.267,0.654,and 0.854,respectively.These experimental data validate that this model has high detection accuracy.

This study aims to analyze the screening results and epidemiological characteristics of prostate cancer (PCa) among elderly males in the rural areas of Songjiang, Shanghai City, through the implementation of a preliminary prostate-specific antigen (PSA) screening based on a community-linkage model, and to explore an effective screening approach. A retrospective observational study design was employed to collect data from residents who underwent PSA screening at Songjiang Hospital affiliated to Shanghai Jiao Tong University School of Medicine, in collaboration with multiple community health service centers in Songjiang District, Shanghai City, between June 2022 and June 2024, through free clinics and annual health examinations. Prostate biopsy was recommended for individuals with total PSA (tPSA) levels >10 ng/ml and those with 4 ng/ml≤tPSA≤10 ng/ml and abnormal free-to-total PSA (f/tPSA) ratios. Clinical characteristics of detected PCa patients were analyzed. Follow-up was conducted through phone calls and home visits by family doctors, coupled with enhanced health education. The results indicated that a total of 17 198 residents participated in the screening, among which 2 234 (12.99%) had tPSA levels between 4 ng/ml and 10 ng/ml, and 257 (1.49%) had tPSA levels >10 ng/ml. Ultimately, 417 residents underwent prostate biopsy, with 171 being diagnosed with PCa, yielding a positive biopsy rate of 41.00% and a PCa detection rate of 0.99%. The predominant pathological subtype among PCa patients was adenocarcinoma (168 cases, 98.24%). Of the 146 PCa patients who received treatment, the majority were classified as intermediate or high-risk (124 cases, 84.93%). Furthermore, with the optimization of the screening model, there was a significant increase in the proportion of subsequent outpatient visits. In conclusion, the community-linkage-based PSA screening model demonstrated high effectiveness in screening for PCa among elderly males in the rural areas of Songjiang, Shanghai City. Epidemiological findings revealed that PCa patients in this region are primarily composed of intermediate and high-risk groups, highlighting the need for intensified early screening and health education.

BACKGROUND:Knee osteoarthritis is a common degenerative disease that significantly impacts patients'quality of life and increases the societal healthcare burden.Early and accurate diagnosis of knee osteoarthritis is crucial for the treatment and prognosis of patients.Traditional diagnostic methods are not only subjective and time-consuming but also do not guarantee consistently high accuracy.OBJECTIVE:To develop an automatic diagnostic method for knee osteoarthritis based on deep learning,utilizing deep learning networks to improve diagnostic accuracy and efficiency.METHODS:A new network model,YOLOV8-ViT,was proposed by replacing the backbone network of YOLOv8n with the Efficient-ViT network and incorporating attention mechanisms for the automatic identification and classification of X-ray images of knee osteoarthritis.The experimental dataset included 5 078 X-ray images of patients with knee osteoarthritis obtained from the Third Affiliated Hospital of Guangzhou University of Chinese Medicine.Three imaging physicians annotated the sites of knee osteoarthritis and classified them according to the Kellgren-Lawrence grading standard using Labelme software,and the results were combined.The evaluation indicators used in this study included Precision,F1 score,mean average precision(mAP),Recall,val/box_loss,val/cls_loss,and val/dfl_loss.RESULTS AND CONCLUSION:The experimental results showed that the YOLOV8-ViT model outperformed the YOLOv5n,YOLOv8n,and YOLOv9n models in terms of precision,mAP50,mAP50-95,F1 score,and Recall,while lowering val/box_loss,val/cls_loss,and val/dfl_loss by 0.496,0.45,and 0.523;1.037,0.305,and 0.728;and 0.267,0.654,and 0.854,respectively.These experimental data validate that this model has high detection accuracy.

The central amygdala (CeA) is a crucial modulator of emotional, behavioral, and autonomic functions, including cardiovascular responses. Despite its importance, the specific circuit by which the CeA modulates blood pressure remains insufficiently explored. Our investigations demonstrate that photostimulation of GABAergic neurons in the centromedial amygdala (CeM^GABA), as opposed to those in the centrolateral amygdala (CeL), produces a depressor response in both anesthetized and freely-moving mice. In addition, activation of CeM^GABA axonal terminals projecting to the nucleus tractus solitarius (NTS) significantly reduces blood pressure. These CeM^GABA neurons form synaptic connections with NTS neurons, allowing for the modulation of cardiovascular responses by influencing the caudal or rostral ventrolateral medulla. Furthermore, CeM^GABA neurons targeting the NTS receive dense inputs from the CeL. Consequently, stimulation of CeM^GABA neurons elicits hypotension through the CeM-NTS circuit, offering deeper insights into the cardiovascular responses associated with emotions and behaviors.
Animals ; GABAergic Neurons/physiology* ; Male ; Central Amygdaloid Nucleus/physiopathology* ; Hypotension/physiopathology* ; Mice ; Blood Pressure/physiology* ; Mice, Inbred C57BL ; Solitary Nucleus/physiology* ; Photic Stimulation ; Neural Pathways/physiology*

Taking Xiaochaihu Decoction as an example,the application differences of classical prescriptions in modern medical context and Chinese medicine practice are compared and analyzed from the aspects of clinical application scope,understanding of pre-scription connotation,dosage specification,dosage form and decoction method.Strategies to solve the differences in efficacy are pro-posed:integrating the wisdom of classical prescriptions and reshaping the framework of Chinese medicine diagnosis and treatment;transforming the results of modern pharmacology and exploring the principles of classical prescriptions;controlling drug quality stand-ards and exploring new uses and dosages of classical prescriptions;keeping pace with the times in Chinese medicine decoction and strengthening management and control to ensure efficacy.It is believed that combining the essence of Chinese medicine with modern technology can make the application of classical prescriptions maintain traditional characteristics while meeting modern clinical require-ments.This can not only improve the adaptability of classical prescriptions to modern complex diseases,but also provide a reference for the modernization of traditional medicine.