ObjectiveTo investigate the mechanism by which Shengjiang Pingchuan Zhike pills （SPZP） alleviate airway injury in the rat model of cough variant asthma （CVA） via regulating autophagy through activating the adenosine monophosphate-activated protein kinase （AMPK）/mammalian target of rapamycin （mTOR）/Unc-51-like autophagy activating kinase 1 （ULK1） signaling pathway based on transcriptomics， thus providing experimental evidence for the clinical application of SPZP. MethodsForty SPF-grade SD rats were enrolled in the present study and randomly via the random number table method assigned into four groups： a blank group， a model group， a SPZP （2 160 mg·kg^-1·d^-1） group， and a positive drug （montelukast sodium， MTL， 0.9 mg·kg^-1·d^-1）group， with 10 rats in each group. After one week of adaptive feeding， the rat model of cough variant asthma （CVA） was established by two-week sensitization with ovalbumin （OVA） and aluminium hydroxide， followed by capsaicin atomization challenge. Rats in the blank group received an equal volume of normal saline during modeling. Upon successful modeling， rats in the SPZP group and positive drug group were administrated with SPZP suspension and MTL solution， respectively， while those in the blank and model groups received the same dose of normal saline by gavage. The whole intervention lasted for 21 consecutive days. After the intervention， general behaviors of rats in each group were observed. Pulmonary function indicators were subsequently detected. Hematoxylin-eosin （HE） staining was performed to visualize the pathological morphology of the lung tissue， and periodic acid-Schiff （PAS） staining was adopted to evaluate airway mucus secretion. Transcriptomic sequencing was conducted to screen differentially expressed genes in the lung tissue across groups. The levels of inflammatory cytokines including interleukin （IL）-4， IL-5， immunoglobulin E （IgE）， and interferon-γ （IFN-γ） in the bronchoalveolar lavage fluid （BALF） were quantified by enzyme-linked immunosorbent assay （ELISA）. Immunofluorescence staining was used for detecting the co-localization of autophagy-related protein Beclin-1 and phosphorylated mammalian target of rapamycin （p-mTOR）. Transmission electron microscopy was employed to count autophagosomes and observe mitochondrial morphological changes in the lung tissue. Western blot assay was utilized to measure the expression levels of AMPK/mTOR/ULK1 axis-related proteins， including AMPK， phosphorylated adenosine monophosphate-activated protein kinase （p-AMPK）， mTOR， phosphorylated mammalian target of rapamycin （p-mTOR）， ULK1， and phosphorylated Unc-51-like autophagy activating kinase 1 （p-ULK1） as well as the autophagy biomarkers Beclin-1 and selective autophagy adaptor protein p62 in lung specimens. ResultsCompared with the blank group， the model group exhibited markedly elevated cough frequency accompanied by wheezing and stridor， dull fur， poor mental activity， diminished motor activity， accelerated respiratory rate， and weakened sensory response and mobility to external stimuli， accompanied by a declining tendency of body mass. Meanwhile， the model group exhibited decreased dynamic lung compliance， increased airway resistance （P<0.01）， severe pathological damage and excessive mucus secretion in the lung tissue （P<0.01）， and elevated levels of IL-4， IL-5， and IgE and declined level of IFN-γ in the BALF （P<0.01）. Ultrastructural observation revealed damaged mitochondrial morphology and a sharp reduction in the number of autophagosomes in the lung tissue. In addition， the expression of p-AMPK， p-ULK1， and Beclin-1 were downregulated， whereas p-mTOR and p62 were highly expressed （P<0.05， P<0.01） in the model group. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analyses demonstrated significant enrichment of AMPK， mTOR， and autophagy-related pathways. Compared with the model group， both the positive drug group and SPZP group obtained visible improvements in general behavioral manifestations and obvious recovery of pulmonary function indicators （P<0.05）， alleviated pulmonary pathological lesions and suppressed excessive airway mucus secretion （P<0.05）， recovered levels of inflammatory cytokines in the BALF （P<0.05）， restored mitochondrial morphology， increased autophagosomes， upregulated expression levels of p-AMPK， p-ULK1， and Beclin-1， and downregulated expression levels of p-mTOR and p62 （P<0.05）. ConclusionSPZP can effectively ameliorate airway inflammation and improve the pulmonary function in the rat model of CVA by activating the AMPK/mTOR/ULK1 signaling pathway， enhancing autophagy in the lung tissue， and inhibiting excessive airway mucus secretion and immune inflammatory imbalance.

Objective:To quantify thoracic and abdominal movements during breathing using inertial measurement units (IMUs) and to build a machine learning model which identifies the abdominal breathing (AB) pattern.Methods:Ten rehabilitation therapists formed the study′s professional group, while 15 patients receiving AB training comprised the validation group. Two synchronized IMUs were applied to capture breathing motions during natural breathing (NB), deep breathing (DB) and AB. Six kinematic features were extracted from each respiratory cycle, and inter-group and inter-pattern differences were analyzed. Correlation analysis was also performed with manually measured changes in thoracic and abdominal circumferences. A support vector classification model for AB pattern detection was then developed using data from the professional and validation groups.Results:A total of 1113 respiratory cycles were extracted and analyzed. The breathing pattern significantly influenced all of the kinematic features studied (0.21≤partial η 2≤0.65, all P≤0.001). The ranges of the angles in medial-lateral axis of the IMUs showed strong correlation with the changes in abdominal and thoracic circumferences (ρ1=0.928, ρ2=0.807, P≤0.001 in both cases). A greater range of abdominal angles was found during AB compared to the other patterns. The best of the models achieved an F1 score of 0.970 (sensitivity: 0.983, specificity: 0.980) in validation. Conclusions:AB generates the greatest abdominal movement. Combining IMUs and machine learning can provide real-time quantification of chest movement and accurate detection of AB during breathing training.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.

OBJECTIVE: Poxviruses are zoonotic pathogens that infect humans, mammals, vertebrates, and arthropods. However, the specific role of ticks in transmission and evolution of these viruses remains unclear. METHODS: Transcriptomic and metatranscriptomic raw data from 329 sampling pools of seven tick species across five continents were mined to assess the diversity and abundance of poxviruses. Chordopoxviral sequences were assembled and subjected to phylogenetic analysis to trace the origins of the unblasted fragments within these sequences. RESULTS: Fifty-eight poxvirus species, representing two subfamilies and 20 genera, were identified, with 212 poxviral sequences assembled. A substantial proportion of AT-rich fragments were detected in the assembled poxviral genomes. These genomic sequences contained fragments originating from rodents, archaea, and arthropods. CONCLUSION Our findings indicate that ticks play a significant role in the transmission and evolution of poxviruses. These viruses demonstrate the capacity to modulate virulence and adaptability through horizontal gene transfer, gene recombination, and gene mutations, thereby promoting co-existence and co-evolution with their hosts. This study advances understanding of the ecological dynamics of poxvirus transmission and evolution and highlights the potential role of ticks as vectors and vessels in these processes.
Animals ; Poxviridae/physiology* ; Ticks/virology* ; Phylogeny ; Transcriptome ; Evolution, Molecular ; Poxviridae Infections/virology* ; Genome, Viral

OBJECTIVE: To analyze the efficacy and safety of decitabine-based myeloablative preconditioning regimen for allogeneic hematopoietic stem cell transplantation (allo-HSCT) in patients with acute myeloid leukemia (AML). METHODS: The clinical characteristics and efficacy of 115 AML patients who underwent allo-HSCT at the First Medical Center of Chinese PLA General Hospital from August 2018 to August 2022 were retrospectively analyzed, including 37 patients treated with decitabine conditioning regimen (decitabine group) and 78 patients without decitabine conditioning regimen (non-decitabine group). The cumulative incidence of relapse (CIR), overall survival (OS), leukemia-free survival (LFS), non-relapse mortality (NRM) and graft versus host disease (GVHD) were analyzed. RESULTS: For the patients in first complete remission (CR1) state before allo-HSCT, the 1-year relapse rates of decitabine group(22 cases) and non-decitabine group(69 cases) were 9.1% and 29.6%, respectively, the difference was statistically significant(P =0.042). The 1-year cumulative incidence of acute graft-versus-host disease (aGVHD) in decitabine group and non-decitabine group was 62.2% and 70.5%, respectively, and the 1-year cumulative incidence of chronic inhibitor-versus-host disease (cGVHD) was 18.9% and 14.1%, respectively, there were no significant differences in the incidence of aGVHD and cGVHD between the two groups (P >0.05). Of the 115 patients, there were no significantly differences in the 1-year CIR(21.7% vs 28.8%, P =0.866), NRM(10.9% vs 3.9%, P =0.203), OS(75.2% vs 83.8%, P =0.131) and LFS(74.6% vs 69.1%, P =0.912) between the decitabine group(37 cases) and the non-decitabine group(78 cases). CONCLUSION Decitabine-based conditioning regimen could reduce the relapse rate of AML CR1 patients with good safety.
Humans ; Leukemia, Myeloid, Acute/therapy* ; Hematopoietic Stem Cell Transplantation/methods* ; Decitabine/therapeutic use* ; Transplantation Conditioning/methods* ; Retrospective Studies ; Graft vs Host Disease ; Transplantation, Homologous ; Male ; Female ; Adult ; Middle Aged ; Adolescent ; Young Adult

Objective:To explore the efficacy and adverse reactions of CAG regimen combined with venetoclax,chidamide,and azacitidine in the treatment of elderly patients with acute myeloid leukemia(AML).Methods:15 elderly AML patients aged ≥ 60 years old who were admitted to the Hematology Department of our hospital from May 2022 to October 2023 were treated with the CAG regimen combined with venetoclax,chidamide and azacitidine,and the efficacy,treatment-related adverse events,overall survival(OS)and event-free survival(EFS)were analyzed.Results:After one course of treatment,11 out of 15 patients achieved complete response(CR),3 patients achieved CR with incomplete hematologic recovery(CRi),and 1 patient died due to prior infection before efficacy evaluation,and the overall response rate(ORR)was 93.3％(14/15).The median follow-up time was 131(19-275)days,with median OS and EFS both remaining unreached.Next-generation sequencing(NGS)analysis showed that among the 15 patients,13 were detected with gene mutations,and there were 7 genes with mutation frequencies of more than 10％,including ASXL1(4 cases),RUNX1(4 cases),BCOR(3 cases),DNMT3A(3 cases),STAG2(2 cases),IDH1/2(2 cases),and TET(2 cases).Among the 13 patients with detectable mutations,12 patients achieved composite response(CR+CRi).The average recovery time of white blood cell count was 14.6 days after chemotherapy,and the average recovery time of platelets was 7.7 days after chemotherapy.The main adverse event was myelosuppression,with 10 patients accompanied by infection.Except for 1 patient who died due to septic shock during chemotherapy,no patients experienced serious complications such as heart,liver,or kidney damage during the treatment process.Conclusion:The CACAG+V regimen,which combines the CAG regimen with venetoclax,chidamide,and azacitidine,can be applied in the treatment of elderly AML patients,demonstrating good safety and induction remission rate.

Objective:To analyze the relationship between microRNA-21(miR-21)expression and the risk of very early relapse post-induction chemotherapy in children with acute lymphoblastic leukemia(ALL).Methods:A total of 110 newly diagnosed children with ALL admitted to Huanggang Central Hospital from March 2020 to September 2022 were included.All patients received induction chemotherapy according to the CCLG-2008 protocol.The patients who achieved complete response(CR)after induction chemotherapy were followed up for 18 months,with very early relapse as the endpoint event.Then the patients were divided into a relapse group and a non-relapse group.Cox regression was used to analyze the influencing factors of very early relapse after induction chemotherapy in children with ALL.ROC curve and decision curve were used to evaluate the predictive value of peripheral blood miR-21 for very early relapse after induction chemotherapy in children with ALL.Restricted cubic splines were used to analyze the dose-response relationship between peripheral blood miR-21 and very early relapse after induction chemotherapy in children with ALL.Results:A total of 102 children with ALL achieved CR after induction chemotherapy,among whom 24 cases(23.53％)experienced very early relapse,with a median relapse time of 14 months.The proportions of patients with high-risk stratification at initial diagnosis,extramedullary infiltration,and minimal residual disease(MRD)positivity were significantly higher in the relapse group than those in the non-relapse group;The absolute lymphocyte count(ALC)in peripheral blood was significantly lower,while the expression levels of miR-21 and lactate dehydrogenase(LDH)were significantly higher in the relapse group compared with the non-relapse group(all P＜0.05).Cox regression analysis showed that very early relapse after induction chemotherapy in children with ALL was associated with medium risk and high risk at initial diagnosis,extramedullary infiltration,decreased ALC in peripheral blood,MRD positivity,as well as high expression levels of miR-21 and LDH(all P＜0.05).ROC curve analysis indicated that the area under the curve(AUC)of peripheral blood miR-21 for predicting very early relapse after induction chemotherapy in children with ALL was 0.800,with an optimal cutoff value of 4.830.Restricted cubic spline analysis revealed that there was a non-linear dose-response relationship between peripheral blood miR-21 and the risk of very early relapse after induction chemotherapy in children with ALL.When the expression level of peripheral blood miR-21 exceeded 4.830,the risk of very early relapse increased with the elevation of miR-21 expression.Decision curve analysis demonstrated that combining peripheral blood miR-21 with other risk factors enhanced the predictive performance for the risk of very early relapse after induction chemotherapy in children with ALL.Conclusion:Very early relapse after induction chemotherapy in children with ALL is associated with elevated expression of miR-21 in peripheral blood,and high expression of miR-21 may increase the risk of very early relapse.Detecting miR-21 before induction chemotherapy has predictive significance for very early relapse in children with ALL,and combining it with other risk factors can improve the predictive efficacy.

Objective:To analyze the relationship between microRNA-21(miR-21)expression and the risk of very early relapse post-induction chemotherapy in children with acute lymphoblastic leukemia(ALL).Methods:A total of 110 newly diagnosed children with ALL admitted to Huanggang Central Hospital from March 2020 to September 2022 were included.All patients received induction chemotherapy according to the CCLG-2008 protocol.The patients who achieved complete response(CR)after induction chemotherapy were followed up for 18 months,with very early relapse as the endpoint event.Then the patients were divided into a relapse group and a non-relapse group.Cox regression was used to analyze the influencing factors of very early relapse after induction chemotherapy in children with ALL.ROC curve and decision curve were used to evaluate the predictive value of peripheral blood miR-21 for very early relapse after induction chemotherapy in children with ALL.Restricted cubic splines were used to analyze the dose-response relationship between peripheral blood miR-21 and very early relapse after induction chemotherapy in children with ALL.Results:A total of 102 children with ALL achieved CR after induction chemotherapy,among whom 24 cases(23.53％)experienced very early relapse,with a median relapse time of 14 months.The proportions of patients with high-risk stratification at initial diagnosis,extramedullary infiltration,and minimal residual disease(MRD)positivity were significantly higher in the relapse group than those in the non-relapse group;The absolute lymphocyte count(ALC)in peripheral blood was significantly lower,while the expression levels of miR-21 and lactate dehydrogenase(LDH)were significantly higher in the relapse group compared with the non-relapse group(all P＜0.05).Cox regression analysis showed that very early relapse after induction chemotherapy in children with ALL was associated with medium risk and high risk at initial diagnosis,extramedullary infiltration,decreased ALC in peripheral blood,MRD positivity,as well as high expression levels of miR-21 and LDH(all P＜0.05).ROC curve analysis indicated that the area under the curve(AUC)of peripheral blood miR-21 for predicting very early relapse after induction chemotherapy in children with ALL was 0.800,with an optimal cutoff value of 4.830.Restricted cubic spline analysis revealed that there was a non-linear dose-response relationship between peripheral blood miR-21 and the risk of very early relapse after induction chemotherapy in children with ALL.When the expression level of peripheral blood miR-21 exceeded 4.830,the risk of very early relapse increased with the elevation of miR-21 expression.Decision curve analysis demonstrated that combining peripheral blood miR-21 with other risk factors enhanced the predictive performance for the risk of very early relapse after induction chemotherapy in children with ALL.Conclusion:Very early relapse after induction chemotherapy in children with ALL is associated with elevated expression of miR-21 in peripheral blood,and high expression of miR-21 may increase the risk of very early relapse.Detecting miR-21 before induction chemotherapy has predictive significance for very early relapse in children with ALL,and combining it with other risk factors can improve the predictive efficacy.

Objective:To quantify thoracic and abdominal movements during breathing using inertial measurement units (IMUs) and to build a machine learning model which identifies the abdominal breathing (AB) pattern.Methods:Ten rehabilitation therapists formed the study′s professional group, while 15 patients receiving AB training comprised the validation group. Two synchronized IMUs were applied to capture breathing motions during natural breathing (NB), deep breathing (DB) and AB. Six kinematic features were extracted from each respiratory cycle, and inter-group and inter-pattern differences were analyzed. Correlation analysis was also performed with manually measured changes in thoracic and abdominal circumferences. A support vector classification model for AB pattern detection was then developed using data from the professional and validation groups.Results:A total of 1113 respiratory cycles were extracted and analyzed. The breathing pattern significantly influenced all of the kinematic features studied (0.21≤partial η 2≤0.65, all P≤0.001). The ranges of the angles in medial-lateral axis of the IMUs showed strong correlation with the changes in abdominal and thoracic circumferences (ρ1=0.928, ρ2=0.807, P≤0.001 in both cases). A greater range of abdominal angles was found during AB compared to the other patterns. The best of the models achieved an F1 score of 0.970 (sensitivity: 0.983, specificity: 0.980) in validation. Conclusions:AB generates the greatest abdominal movement. Combining IMUs and machine learning can provide real-time quantification of chest movement and accurate detection of AB during breathing training.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.