Objective To investigate the relationship between the mean platelet volume(MPV)to platelet count(PLT)ratio(MPV/PLT),blood urea nitrogen(BUN)to lipoprotein a[Lp(a)]ratio[BUN/Lp(a)]and the prognosis of patients with acute exacerbation of chronic obstructive pulmonary disease(COPD).Methods A total of 106 patients with acute exacerbation of COPD admitted to the hospital from January 2021 to January 2024 were selected as the research objects.According to the prognosis,they were divided into sur-vival group(72 cases)and death group(34 cases).The results of routine laboratory tests,blood lipid and lipo-protein levels were compared between the two groups.Multivariate Logistic regression was used to analyze the influencing factors of death in patients with acute exacerbation of COPD.The receiver operating characteristic(ROC)curve was used to evaluate the predictive value of MPV/PLT and BUN/Lp(a)for the prognosis of pa-tients with acute exacerbation of COPD.Results Compared with the survival group,the invasive ventilation rate,acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score,C reactive protein(CRP),white blood cell count(WBC),MPV,BUN,MPV/PLT and BUN/Lp(a)were significantly increased in the death group(P＜0.05).The non-invasive ventilation rate,lymphocyte count,PLT and Lp(a)levels were signifi-cantly decreased(P＜0.05).Multivariate Logistic regression analysis showed that APACHE Ⅱ score,CRP,WBC,lymphocyte count,MPV,PLT,MPV/PLT,BUN,Lp(a)and BUN/Lp(a)were the influencing factors of death in patients with acute exacerbation of COPD(P＜0.05).ROC curve results showed that the sensitivity and specificity of MPV/PLT combined with BUN/Lp(a)for predicting the prognosis of patients with acute exacerbation of COPD were 88.2％and 84.7％,respectively,and the area under curve was 0.887.Conclusion MPV/PLT and BUN/Lp(a)are closely related to the prognosis of patients with acute exacerbation of COPD.The combination of MPV/PLT and BUN/Lp(a)has a high predictive value for the prognosis of patients.

ObjectiveTo analyze the influencing factors for recurrence in successfully treated pulmonary tuberculosis patients with isoniazid-resistant and rifampicin-sensitive in Shaoxing City, Zhejiang Province. MethodsData on general demographic information, treatment information and drug susceptibility test results for pulmonary tuberculosis patients admitted to the designated tuberculosis medical institutions and registered in the tuberculosis information management system was collected in Shaoxing City from January 2011 to August 2024. A total of 428 patients with isoniazid resistance (including isoniazid single resistance and multiple resistance) but who were successfully treated were included in the study. Information for the recurrence after successful treatment of the patients was analyzed. The Cox proportional hazards models were used to analyze the influencing factors of recurrence in patients. ResultsAmong the 428 successfully treated patients included in the study, 31 cases (accounting for 7.24%) had recurrence by the end of the observation period, with a recurrence rate density of 1.31 per 100 person-years and a median recurrence time of 0.99 (0.08, 8.27) years. Among the relapsed population, 51.61% of the patients relapsed within one year after successful treatment. 77.42% of the patients relapsed within two years after successful treatment. Multivariate Cox regression analysis showed that when isoniazid resistance was discovered, the diagnosis classification of relapse (HR=4.115, 95%CI: 1.734‒9.767) and positive 0-month sequence smear (HR=4.457, 95%CI: 1.053‒18.866) were risk factors for recurrence after successful treatment in patients. ConclusionRegular follow-up should be strengthened for at least two years after the successful treatment of isoniazid-resistant pulmonary tuberculosis patients. Special attention should be paid to the treatment effect and regular re-examination and monitoring after the end of the treatment course of isoniazid-resistant pulmonary tuberculosis patients who have been re-treated and were sputum smear positive at baseline, so as to prevent recurrence and disease progression in high-risk populations.

ObjectiveTo explore the mechanism of the anti-cancer compound formula Feiyanning in inhibiting epithelial-mesenchymal transition （EMT） and invasion and metastasis of non-small cell lung cancer （NSCLC）. MethodsCell proliferation and activity were assessed using the cell counting kit-8（CCK-8） assay to evaluate the effect of Feiyanning on the proliferation of A549 and H1299 cells. Wound healing and Transwell assays were conducted to examine Feiyanning's impact on the metastasis of A549 and H1299 cells. The effects of Feiyanning on EMT and the transforming growth factor-β₁ （TGF-β₁）/Smad signaling pathway proteins in A549 and H1299 cells were detected by Western blot. Exogenous TGF-β₁ was used to induce EMT in A549 and H1299 cells. The effects of Feiyanning on TGF-β₁-induced NSCLC cell metastasis， EMT， and the TGF-β₁/Smad pathway proteins were assessed by wound healing assay， Transwell assay， and Western blot. In vivo， an A549 lung metastasis model was established via tail vein injection in nude mice. A total of 28 SPF male nude mice were randomly divided into four groups： Model （NC） group， Feiyanning low-dose （FYN1） group， Feiyanning high-dose （FYN2） group， and the positive control group （TGF-β receptor kinase inhibitor SB431542 group）. The corresponding interventions were performed. After 40 days， the mice were euthanized， and lung metastases were analyzed. The expression of E-cadherin， N-cadherin， p-Smad2， and p-Smad3 in each group was detected by immunohistochemistry （IHC）. ResultsAfter Feiyanning intervention， compared to the blank group， Feiyanning inhibited the proliferation of A549 and H1299 cells in a concentration-dependent manner （P<0.01）. The metastasis ability of Feiyanning-treated cells was significantly decreased compared to the blank group （P<0.01）. The expression of EMT marker proteins N-cadherin and zinc finger transcription factors （Zeb1， Snail， Slug） was significantly reduced in the Feiyanning groups compared to the blank group （P<0.05， P<0.01）. The expression of p-Smad2/3， Smad2/3， TβRI， and TβRⅡ， key proteins in the TGF-β₁/Smad signaling pathway， was also significantly decreased （P<0.01）. In the TGF-β₁-induced EMT model， compared to the TGF-β₁ group， the cell metastasis ability in the Feiyanning groups was reduced （P<0.01）， and the expression levels of N-cadherin， Zeb1， Snail， and Slug were significantly lower （P<0.01）. The expression levels of p-Smad2/3， Smad2/3， TβRI， and TβRⅡ were also significantly reduced （P<0.01）. In vivo results showed that compared to the model group， the number of lung metastases in the FYN1， FYN2， and SB431542 groups was reduced （P<0.01）， and the range of cell infiltration was narrowed. Immunohistochemical results showed that compared to the model group， the expression of E-cadherin in the FYN1， FYN2， and SB431542 groups was increased （P<0.01）， the expression of N-cadherin decreased （P<0.05， P<0.01）， and the expression of p-Smad2 and p-Smad3， key proteins of the TGF-β₁/Smad pathway， was reduced （P<0.01）. ConclusionFeiyanning inhibits the invasion and metastasis of NSCLC cells and EMT. The mechanism is related to the inhibition of TGF-β₁/Smad signaling pathway.

ObjectiveTo explore the mechanism of the anti-cancer compound formula Feiyanning in inhibiting epithelial-mesenchymal transition （EMT） and invasion and metastasis of non-small cell lung cancer （NSCLC）. MethodsCell proliferation and activity were assessed using the cell counting kit-8（CCK-8） assay to evaluate the effect of Feiyanning on the proliferation of A549 and H1299 cells. Wound healing and Transwell assays were conducted to examine Feiyanning's impact on the metastasis of A549 and H1299 cells. The effects of Feiyanning on EMT and the transforming growth factor-β₁ （TGF-β₁）/Smad signaling pathway proteins in A549 and H1299 cells were detected by Western blot. Exogenous TGF-β₁ was used to induce EMT in A549 and H1299 cells. The effects of Feiyanning on TGF-β₁-induced NSCLC cell metastasis， EMT， and the TGF-β₁/Smad pathway proteins were assessed by wound healing assay， Transwell assay， and Western blot. In vivo， an A549 lung metastasis model was established via tail vein injection in nude mice. A total of 28 SPF male nude mice were randomly divided into four groups： Model （NC） group， Feiyanning low-dose （FYN1） group， Feiyanning high-dose （FYN2） group， and the positive control group （TGF-β receptor kinase inhibitor SB431542 group）. The corresponding interventions were performed. After 40 days， the mice were euthanized， and lung metastases were analyzed. The expression of E-cadherin， N-cadherin， p-Smad2， and p-Smad3 in each group was detected by immunohistochemistry （IHC）. ResultsAfter Feiyanning intervention， compared to the blank group， Feiyanning inhibited the proliferation of A549 and H1299 cells in a concentration-dependent manner （P<0.01）. The metastasis ability of Feiyanning-treated cells was significantly decreased compared to the blank group （P<0.01）. The expression of EMT marker proteins N-cadherin and zinc finger transcription factors （Zeb1， Snail， Slug） was significantly reduced in the Feiyanning groups compared to the blank group （P<0.05， P<0.01）. The expression of p-Smad2/3， Smad2/3， TβRI， and TβRⅡ， key proteins in the TGF-β₁/Smad signaling pathway， was also significantly decreased （P<0.01）. In the TGF-β₁-induced EMT model， compared to the TGF-β₁ group， the cell metastasis ability in the Feiyanning groups was reduced （P<0.01）， and the expression levels of N-cadherin， Zeb1， Snail， and Slug were significantly lower （P<0.01）. The expression levels of p-Smad2/3， Smad2/3， TβRI， and TβRⅡ were also significantly reduced （P<0.01）. In vivo results showed that compared to the model group， the number of lung metastases in the FYN1， FYN2， and SB431542 groups was reduced （P<0.01）， and the range of cell infiltration was narrowed. Immunohistochemical results showed that compared to the model group， the expression of E-cadherin in the FYN1， FYN2， and SB431542 groups was increased （P<0.01）， the expression of N-cadherin decreased （P<0.05， P<0.01）， and the expression of p-Smad2 and p-Smad3， key proteins of the TGF-β₁/Smad pathway， was reduced （P<0.01）. ConclusionFeiyanning inhibits the invasion and metastasis of NSCLC cells and EMT. The mechanism is related to the inhibition of TGF-β₁/Smad signaling pathway.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

Purpose To explore the feasibility and clinical value of deep learning-based image reconstruction technology in 5.0T MRI for nasopharyngeal carcinoma.Materials and Methods A prospective study was conducted on 50 newly diagnosed nasopharyngeal carcinoma patients from August to December 2024 at Sun Yat-sen University Cancer Center.5.0T MRI was performed to scan the nasopharynx region.Routine scanning protocols included transverse T2WI,transverse T1WI,transverse contrast-enhanced T1WI and coronal fat-suppressed contrast-enhanced T1WI sequences.Based on these standard scanning protocols,DeepRecon deep learning reconstruction technology with different levels(grade 1-5)was applied,generating a total of 24 sets of images.Qualitative evaluation employed a Likert scale(5-point system)for subjective scoring on lesion detection,lesion edge clarity,artifacts and overall image quality.Quantitative evaluation was performed using the signal-to-noise ratio and contrast-to-noise ratio to objectively assess the quality of the 24 image sets.Differences in qualitative and quantitative indicators between different groups were compared,while the Kappa coefficient was used to analyze the consistency of subjective evaluations by two radiologists.Results In the qualitative assessment of 24 image sets from four MRI sequences(with and without DeepRecon reconstruction),DeepRecon images(grade 2-4)significantly outperformed traditional images in all features except for artifact reduction(Z=-12.11--6.23,all P<0.001).Images reconstructed at DeepRecon grade 3 had the highest overall score and the best image quality.Furthermore,compared with traditional images,DeepRecon images(grade 2-5)demonstrated significantly improved signal-to-noise ratio for both lesions and the lateral pterygoid muscle(t=-15.67--3.44,Z=-6.09--4.63,all P<0.01).In addition,in the transverse T2WI,transverse contrast-enhanced T1WI and coronal fat-suppressed contrast-enhanced T1WI images with DeepRecon reconstruction(grade 2-5),the contrast-to-noise ratio(lesion/lateral pterygoid muscle)also showed significant improvement compared to traditional images(t=-12.71--3.19,Z=-6.08--4.47,all P<0.001).The inter-observer agreement for the overall subjective quality score between the two radiologists was good(Kappa=0.75-0.82,all P<0.01).Conclusion DeepRecon deep learning reconstruction technology significantly increases the signal-to-noise ratio and resolution of traditional magnetic resonance images of nasopharyngeal cancer,improving image clarity and bringing more possibilities for the advancement of imaging diagnosis.

Objective To explore the mechanism of Kangliu Zengxiao Jiandu Prescription(KLJD)in enhancing the efficacy of cisplatin chemotherapy in non-small cell lung cancer(NSCLC)through network pharmacology and in vivo/in vitro experiments.Methods Components of KLJD were screened via the TCMSP database to identify active components and potential targets.Lung cancer-related genes were obtained from the GeneCards and OMIM databases.GO and KEGG pathway enrichment analysis was performed on drug-disease intersection targets using the Metascape database;molecular docking was performed between core target proteins and main active components.A Lewis lung cancer mouse model was established,and intervened with KLJD and cisplatin.Organ indexes and tumor inhibition rate were counted,and Western blot and RT-PCR were used to detect the expressions of key pathway target proteins and mRNA;A549 and H1299 cells were intervened with KLJD,and Western blot was used to detect key target protein expressions.Results Network pharmacology identified 74 active components and 20 key targets of KLJD,primarily involved in biological processes such as cell proliferation and inflammatory response,and pathways in cancer and PI3K/AKT signaling pathway;molecular docking revealed stable binding between EGFR and major compounds.Animal experiments demonstrated that,compared with the model group,the KLJD group showed significantly higher tumor inhibition rate(P<0.01)and downregulation of EGFR,AKT and PI3K protein and mRNA expression in tumor tissues(P<0.05).Compared with the cisplatin group,the combination group exhibited significantly enhanced tumor inhibition rate(P<0.01),elevated thymic and splenic indices(P<0.01),and decreased EGFR,PI3K and AKT protein and mRNA expressions(P<0.01).Cell experiments showed that KLJD concentration-dependently inhibited A549 and H1299 cell proliferation(IC50:14.72 mg/mL and 14.68 mg/mL,respectively).Combined with cisplatin,KLJD synergistically down-regulated EGFR PI3K and AKT protein expressions(P<0.01).Conclusion KLJD effectively enhances cisplatin's chemotherapeutic efficacy in NSCLC by inhibiting the EGFR/PI3K/AKT pathway while improving immune organ function.Its mechanism likely involves multi-target regulation,including suppression of tumor proliferation,promotion of apoptosis,and modulation of the immune microenvironment.

Drug development encompasses multiple processes,wherein protein subcellular localization is essential.It promotes target identification,treatment development,and the design of drug delivery systems.In this research,a deep learning framework called LocPro is presented for predicting protein subcellular localization.Specifically,LocPro is unique in(a)combining protein representations from the pre-trained large language model(LLM)ESM2 and the expert-driven tool PROFEAT,(b)implementing a hybrid deep neural network architecture that integrates convolutional neural network(CNN),fully connected(FC)layer,and bidirectional long short-term memory(BiLSTM)blocks,and(c)developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels.Additionally,a dataset was curated and divided using a homology-based strategy for training and validation.Compar-ative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction.The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization.All in all,LocPro serves as a valuable complement to existing protein localization prediction tools.The web server is freely accessible at https://idrblab.org/LocPro/.

Objective To explore the mechanism of Kangliu Zengxiao Jiandu Prescription(KLJD)in enhancing the efficacy of cisplatin chemotherapy in non-small cell lung cancer(NSCLC)through network pharmacology and in vivo/in vitro experiments.Methods Components of KLJD were screened via the TCMSP database to identify active components and potential targets.Lung cancer-related genes were obtained from the GeneCards and OMIM databases.GO and KEGG pathway enrichment analysis was performed on drug-disease intersection targets using the Metascape database;molecular docking was performed between core target proteins and main active components.A Lewis lung cancer mouse model was established,and intervened with KLJD and cisplatin.Organ indexes and tumor inhibition rate were counted,and Western blot and RT-PCR were used to detect the expressions of key pathway target proteins and mRNA;A549 and H1299 cells were intervened with KLJD,and Western blot was used to detect key target protein expressions.Results Network pharmacology identified 74 active components and 20 key targets of KLJD,primarily involved in biological processes such as cell proliferation and inflammatory response,and pathways in cancer and PI3K/AKT signaling pathway;molecular docking revealed stable binding between EGFR and major compounds.Animal experiments demonstrated that,compared with the model group,the KLJD group showed significantly higher tumor inhibition rate(P<0.01)and downregulation of EGFR,AKT and PI3K protein and mRNA expression in tumor tissues(P<0.05).Compared with the cisplatin group,the combination group exhibited significantly enhanced tumor inhibition rate(P<0.01),elevated thymic and splenic indices(P<0.01),and decreased EGFR,PI3K and AKT protein and mRNA expressions(P<0.01).Cell experiments showed that KLJD concentration-dependently inhibited A549 and H1299 cell proliferation(IC50:14.72 mg/mL and 14.68 mg/mL,respectively).Combined with cisplatin,KLJD synergistically down-regulated EGFR PI3K and AKT protein expressions(P<0.01).Conclusion KLJD effectively enhances cisplatin's chemotherapeutic efficacy in NSCLC by inhibiting the EGFR/PI3K/AKT pathway while improving immune organ function.Its mechanism likely involves multi-target regulation,including suppression of tumor proliferation,promotion of apoptosis,and modulation of the immune microenvironment.

Objective:To investigate the contributing factors to elevated blood pressure in borderline cases during medical selection for recruitment of Air Force flying cadets in order to enhance the accuracy of selection.Methods:Blood pressure was measured among 2 350 male high school graduates in the 2022 re-selection phase of medical selection of Air Force flying cadets. None of the participants had a family history of hypertension according to previous health checkups. Identified through blood pressure measurement, subjects with borderline hypertension were assigned to an intervention group (self-intervention with personalized correction plans) and a control group (self-intervention alone) using a random number table. Standardized blood pressure measurements and comprehensive medical history reviews were performed to compare pre- and post-intervention outcomes across the 2 groups, followed by an investigation into the causative mechanisms of elevated blood pressure.Results:Among the 102 cases of borderline hypertension (51 per group) identified, primary contributing factors included the white-coat phenomenon (41.2%), pre-examination physical activity (17.6%), pre-examination medications (3.9%) and poor sleep quality (35.3%). No significant differences in systolic blood pressure (SBP) or diastolic blood pressure (DBP) were observed between the 2 groups at baseline (both P>0.05). After interventions, the intervention group showed significantly lower SBP ( t=3.13, P=0.002) and DBP ( t=7.68, P<0.001) than the control group. Both groups exhibited reductions in SBP and DBP from baseline ( t=6.63, 8.97, 4.13, 2.03, P<0.001, <0.001, <0.001, =0.043). The percentage of students with normal blood pressure was 96.1% (49/51) in the intervention group and 78.4% (40/51) in the control group. Conclusions:Transient blood pressure elevation in selection settings primarily stems from the white-coat phenomenon, physical exertion, medications and sleep disturbances. Standardizing blood pressure measurement protocols and addressing transient factors can help avoid unwarranted disqualifications and ensure the accuracy of selection.