ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

Objective: To explore the impact of donor reentry experience, specifically among those with a single reactive serological result who completed the reentry process, on their willingness to return for future blood donation, and to examine the mediating roles of blood donation knowledge and trait anxiety. Methods: A cross-sectional survey was conducted between November and December 2025. A total of 386 blood donors from the Changsha Blood Center were categorized into a reentry group (n=123) and a control group (n=263). Data on demographic characteristics, blood donation knowledge (BDKQ), trait anxiety (STAI-T), and blood donation return intention (BDRIS) were collected via questionnaires. SPSS 27.0 and AMOS 28.0 were used for statistical analyses, including independent-samples t-test, one-way ANOVA, multiple linear regression, and path analysis for mediating effect testing. Results: There were statistically significant differences in age, occupation, education level, monthly income and donation frequency between the reentry group and the control group (all P<0.05). The reentry group scored significantly higher in blood donation knowledge and blood donation return intention than the control group (both P<0.05). The mean BDRIS score was 11.51±3.62, indicating a relatively high intention to return. Blood donation knowledge was significantly negatively correlated with trait anxiety (r=-0.15, P<0.05) and positively correlated with blood donation return intention (r=0.19, P<0.05); trait anxiety was significantly negatively correlated with blood donation return intention (r=-0.33, P<0.05). Significant differences in BDRIS scores were found based on group (reentry vs control), age, and number of previous donations (all P<0.05). Multiple linear regression showed that BDKQ positively predicted BDRIS (β=0.11, P<0.05), while STAI-T negatively predicted BDRIS (β=-0.27, P<0.05). Path analysis further revealed that the reentry experience had no direct effect on the intention to return. However, it exerted a positive influence through two indirect pathways: 1) a simple mediating effect via increased blood donation knowledge (β=0.17, accounting for 25.0% of the total effect), and 2) a chain mediating effect through "increased blood donation knowledge → decreased trait anxiety" (β=0.05, accounting for 8.1% of the total effect). The model fit indices reached the ideal fitting criteria. Conclusion: The donor reentry experience does not directly enhance the intention to return for blood donation. Rather, it may exert an indirect positive influence by increasing blood donation knowledge and through the sequential pathway of "increased knowledge → decreased trait anxiety". Blood collection institutions should leverage the reentry process as an opportunity for education and psychological support to improve donor retention rate.

Objective: To investigate the correlation between color parameters (L^*, a^*, b^*, Eab^*) and the contents of reducing sugars, total polysaccharides, total oligosaccharides, as well as four saccharides (fructose, glucose, sucrose, and kestose) during the nine cycles of steaming and sun-drying processing of Polygonatum kingianum, and to preliminarily explore the optimal processing duration.
Methods: The color changes were objectively evaluated using a colorimeter. The anthrone-sulfuric acid method was employed to determine total polysaccharides and oligosaccharides. The 3,5-dinitrosalicylic acid (DNS) colorimetric method was used to measure total reducing sugar content. High-performance liquid chromatography coupled with charged aerosol detection (HPLC-CAD) was applied for quantitative analysis of fructose, glucose, sucrose, and kestose. Multivariate statistical analysis was conducted to assess samples from different processing stages.
Results: Significant variations in color and component contents were observed across processing stages. The herbal pieces progressively darkened with increased processing cycles: brightness (L^*) and total color difference (Eab^*) initially decreased then stabilized, while a^* (red-green) and b^* (yellow-blue) values first increased then declined. Total polysaccharides and oligosaccharides showed overall decreasing trends, whereas reducing sugars initially increased before stabilizing. Fructose and glucose levels rose continuously, while sucrose and kestose decreased progressively, becoming undetectable after five cycles.
Conclusion: The chromatic alteration and saccharide composition of P. kingianum showed significant correlation with processing duration. Both total color difference (Eab^*) and reducing sugar content stabilized after four processing cycles (12 hours), suggesting that four cycles of steaming and sun-drying may represent the optimal processing duration.

Objective: To investigate the correlation between the color parameters (L^*, a^*, b^*, Eab^* values) of Polygoni Multiflori Radix Praeparata powder prepared by different processing techniques and the contents of 2,3,5,4’-tetrahydroxystilbene-2-O-β-D-glucopyranoside, emodin, physcion, emodin-8-O-β-D-glucopyranoside, physcion-8-O-β-D-glucopyranoside.
Methods: The L^*, a^*, b^* and Eab^* values of Polygoni Multiflori Radix Praeparata powder prepared by different processing techniques were determined by spectrophotometer, and the contents of the five components were determined by high performance liquid chromatography. Secondly, SPSS 26.0 software and Simca 14.1 software were used to analyze the correlation.
Results: Through the hierarchical cluster analysis (HCA), it was found that the steamed samples and black bean steamed samples could be obviously divided into two categories: raw products and processed products. The processed products could be further divided into 2-8 h and 12-48 h. Pearson correlation analysis showed that the content of stilbene glycoside was significantly positively correlated with L^*, a^* and b^* values (P<0.01). The a^* value was significantly positively correlated with the content of emodin and physcion (P<0.01). Emodin-8-O-β-D-glucoside was positively correlated with L^* value and b^* value, while physcion-8-O-β-D-glucoside was negatively correlated with a^* value. Partial least squares discriminant analysis (PLS) showed that 2,3,5,4’-tetrahydroxystilbene-2-O-β-D-glucoside (VIP=1.69) and emodin-8-O-β-D-glucoside (VIP=1.06) were the key variables affecting chromaticity characteristics (P<0.01).
Conclusion: The three processes of steaming, black bean steaming and black bean stewing are consistent in composition transformation and chromaticity variation, and stilbene glycoside can be used as a specific index component to characterize the processed color. Chromatic parameters can effectively reflect the processing progression and serve as quality monitoring indicators during production.

BackgroundObstructive sleep apnea （OSA） represents a prevalent sleep disordered breathing condition characterized by complex pathophysiology. The arousal threshold （ArTH）， a core non-anatomical contributor to OSA pathogenesis， is intimately tied to the disease severity and clinical phenotypes. To date， research regarding factors associated with ArTH has yielded inconsistent findings， and ArTH profiles and disparities across populations with different genetic backgrounds are not fully elucidated. ObjectiveTo explore the differences of ArTH in OSA patients with different genetic backgrounds and analyze the key factors affecting ArTH， thereby providing evidence for understanding OSA pathophysiology and formulating targeted treatment regimens. MethodsA total of 285 patients who met the diagnostic criteria for OSA， as defined by the Multidisciplinary Diagnosis and Treatment Guidelines for Adult Obstructive Sleep Apnea， were retrospectively enrolled in this study. All participants underwent overnight polysomnography （PSG） at the Sleep Medicine Center of Inner Mongolia Mental Health Center from December 2022 to May 2024. Based on the study design， the cohort was stratified into two distinct genetic background subgroups （group A and group B）. Demographic and clinical characteristics， the Epworth Sleepiness Scale （ESS） score， and overnight PSG data were collected. The apnea hypopnea index （AHI）， the lowest pulse oxygen saturation （LSpO₂）， and fraction of hypopneas （F_Hypopneas） were utilized as surrogate indicators to estimate ArTH in OSA patients. The influencing factors of low ArTH were tested by binary Logistic regression analysis. ResultsAmong the 285 OSA patients， there were 227 cases （79.65%） in group A and 58 cases （20.35%） in group B. Comparisons between the two genetic background subgroups revealed no statistically significant differences in the proportion of low ArTH， ESS score， PSG parameters， and the three markers for low ArTH （AHI<30 events/h， LSpO₂>82.5%， F_Hypopneas>58.3%） （P>0.05）. Binary Logistic regression analysis identified sex （OR=2.421， 95% CI： 1.070–5.478）， BMI （OR=0.847， 95% CI： 0.770–0.932）， N1 sleep duration （OR=0.974， 95% CI： 0.963–0.985）， and hypertension （OR=0.348， 95% CI： 0.143–0.848） as independent factors associated with low ArTH in the total cohort. Stratified analysis by genetic backgrounds revealed that sex（OR=3.799， 95% CI： 1.389–10.392）， BMI（OR=0.819， 95% CI：0.723–0.929）， and N1 sleep duration（OR=0.973， 95% CI： 0.961–0.986） were independent factors of low ArTH in group A. In contrast， only N1 sleep duration （OR=0.951， 95% CI： 0.911–0.993） remained a significant factor in group B. ConclusionArthur may have conservative characteristics in patients with OSA with different genetic backgrounds， but the pathophysiological mechanism of OSA may have population heterogeneity. ［Funded by Inner Mongolia Autonomous Region Natural Science Fundation Project （number， 2024QN08050）； Intra-institutional Scientific Research Project of Inner Mongolia Mental Health Center （number， 2022QNWN0010）］

ObjectiveTo investigate the value of third lumbar skeletal muscle mass index （L3-SMI） in predicting the long-term prognosis of patients with acute-on-chronic liver failure （ACLF）， and to provide a useful tool for prognostic scoring of ACLF patients. MethodsA retrospective analysis was performed for the data of 126 patients who underwent abdominal computed tomography （CT） scanning and were diagnosed with ACLF in Shanxi Bethune Hospital from December 2017 to December 2021， including clinical indicators， biochemical parameters， and model for end-stage liver disease （MELD） score， and L3-SMI was calculated. The independent-samples t test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was used to assess the diagnostic value of L3-SMI and other variables （MELD score and Child-Pugh score）， and the DeLong test was used for comparison of the area under the ROC curve （AUC）. ResultsAmong the 126 patients enrolled， 44 （35%） died within 2 years and 82 （65%） survived. Compared with the survival group， the death group had significantly higher age， incidence rate of ascites， international normalized ratio， MELD score， and Child-Pugh score （all P<0.05） and a significantly lower value of L3-SMI ［38.40 （35.95‍ ‍—‍ ‍46.29） cm²/m² vs 44.19 （40.20‍ ‍—‍ ‍48.58） cm²/m²， Z=-2.855， P=0.004］. L3-SMI had an AUC of 0.720 in predicting 2-year mortality in ACLF patients， with a sensitivity of 63.6% and a specificity of 80.5%， and a combination of L3-SMI， MELD score， and Child-Pugh score had a significantly better AUC than a combination of MELD score and Child-Pugh score in predicting 2-year mortality （0.809 vs 0.757， Z=2.015， P<0.05）. ConclusionL3-SMI has a high predictive value for the prognosis of ACLF patients， and the combination of L3-SMI、MELD score and Child-Pugh score has a higher predictive value for ACLF patients， and the inclusion of L3-SMI or sarcopenia in the conventional prognostic scores of ACLF patients may increase the ability to predict disease progression.

Multiple system atrophy (MSA) is a rare neurodegenerative disease with complex clinical manifestations, presenting substantial challenges in clinical diagnosis and treatment. Its symptoms and the eight extraordinary meridians are potentially correlated; therefore, this article explores the association between MSA symptom clusters and the eight extraordinary meridians based on their circulation and physiological functions, as well as their treatment strategies. The progression from deficiency to damage in the eight extraordinary meridians aligns with the core pathogenesis of MSA, which is characterized by "the continuous accumulation of impacts from the vital qi deficiency leading to eventual damage". Liver and kidney deficiency and the emptiness of the eight extraordinary meridians are required for the onset of MSA; the stagnation of qi deficiency and the gradual damage to the eight extraordinary meridians are the key stages in the prolonged progression of MSA. The disease often begins with the involvement of the yin and yang qiao mai, governor vessel, thoroughfare vessel, and conception vessel before progressing to multiple meridian involvements, ultimately affecting all eight extraordinary meridians simultaneously. The treatment approach emphasizes that "the direct method may be used for joining battle, but indirect method will be needed in order to secure victory" and focuses on "eliminate pathogenic factors and reinforce healthy qi". Distinguishing the extraordinary meridians and focusing on the primary symptoms are pivotal to improving efficacy. Clinical treatment is aimed at the target, and tailored treatment based on careful clinical observation ensures precision in targeting the disease using the eight extraordinary meridians as the framework and core symptoms as the specific focus. Additionally, combining acupuncture, daoyin therapy, and other method may help prolong survival. This article classifies clinical manifestations based on the theory of the eight extraordinary meridians and explores treatment.

Objective:To detect the expression levels of long non-coding RNAs(lncRNA)in elderly patients with pulmonary tuberculosis(PTB)and those with non-tuberculous lung diseases(non-TB), and to assess the performance of these lncRNA in the differential diagnosis of PTB.Methods:A total of 300 elderly patients with suspected PTB were recruited from Beijing Chest Hospital between January 2024 and September 2024, and were further divided into the PTB group and the non-TB lung disease group based on the results of mycobacterium tuberculosis(MTB)pathogenicity testing.Peripheral blood mononuclear cells were isolated using a lymphocyte separation solution, and RNA was extracted using the TRIzol method.Nine lncRNAs, previously identified as differentially expressed in PTB through our group's microarray analysis, were selected and detected by real-time fluorescence quantitative polymerase chain reaction to evaluate the expression levels of these lncRNAs between the PTB and non-TB lung disease groups.The overall patients were randomly divided into training and validation sets in a 7∶3 ratio.Lasso regression was employed to select the characteristic variables, and a random forest algorithm was then used to construct the lncRNA diagnostic portfolio.Receiver operating characteristic(ROC)curves were generated to evaluate the diagnostic performance of individual lncRNAs and the combined panel in differentiating elderly patients with PTB from those with other non-TB lung diseases.Results:A total of 201 cases were included, with 105 confirmed elderly patients diagnosed with PTB(52.2%)and 96 elderly patients suffering from non-TB lung disease(47.8%).Compared to the elderly patients with non-TB lung disease, the expression levels of ENST00000417346.1, ENST00000620744.1, lncRNA PWP1, ENST00000583184.1, lncRNA ABHD17B, ENST00000607464.1, ENST00000516057.1, and NR_003000 were significantly downregulated in the PTB patients, whereas the expression level of lncRNA BCL2L10 was significantly upregulated in the PTB patients.ROC analysis revealed that the area under the curve(AUC)for each lncRNA ranged from 0.659 to 0.848.The diagnostic panel, which included NR_003000, ENST00000607464.1, ENST00000583184.1, and ENST00000620744.1 as determined by Lasso analysis, exhibited AUC values of 0.917 and 0.906 in the training and validation sets, respectively.The performance of this panel was superior to that of each individual lncRNA.Conclusions:The random forest model, which incorporates NR_003000, ENST00000607464.1, ENST00000583184.1, and ENST00000620744.1, demonstrates potential in differentiating between PTB and non-TB lung diseases.