ObjectiveGastrodia elata has evolved ecological types with shortened rhizome internodes and diversified flower and fruit coloration in response to different altitudes. Studying the genetic mechanisms of different ecotype germplasm is significant for guiding variety breeding in different cultivation areas. MethodsThe bHLH gene family was identified based on the whole-genome datasets of G. elata f. elata and G. elata f. glauca. Subsequently， the gene family members were subject to analysis， including gene structure， chromosomal localization， cis-acting elements， gene synteny， and phylogeny. Combined with transcriptome data and quantitative Real-time PCR， the expression patterns of bHLH genes in the stems of the different G. elata ecotype germplasm were analyzed. Finally， correlation analysis was conducted between gene expression patterns and color to obtain the key bHLH genes regulating the color formation of stem. ResultsA total of 63 bHLH genes were identified in both G elata f. elata and G. elata f. glauca， unevenly distributed across 17 chromosomes and clustered into 16 subfamilies， with significant expansion in some family members. Obvious inversions of bHLH genes on the same chromosome and interchromosomal translocations were detected in the two ecotype germplasm. Among these genes， 12 bHLH genes （such as bHLH62-3 and bHLH74） were associated with the bright yellow color of G elata f. elata stem， while 9 bHLH genes （such as PIL13， UNE12， and bHLH130） were correlated with the red color of G. elata f. glauca stem. Compared to G. elata f. glauca， the bHLH48 expression level was significantly higher in flowers and scale leaves of G elata f. elata， and the bHLH62-3 expression level was significantly higher in all organs of G elata f. elata. ConclusionsFunctional pathway divergence of the bHLH family members has occurred across different chromosomes in G elata f. elata and G. elata f. glauca. Through synergism or antagonism with other genes， 21 bHLH genes participate in the coloration metabolic pathway regulation of stems， flowers， and fruits. Specifically， bHLH62-3 is involved in regulating stem color differentiation in the anthocyanin biosynthesis pathway of G. elata， thus relevant to the color formation of stem. Additionally， GebHLH48 positively regulates flowering-related pathways to promote the early-flowering phenotype of G. elata f. elata. These findings have laid the foundation for analyzing the genetic regulatory mechanisms underlying the color formation of the G. elata stem.

ObjectiveGastrodia elata has evolved ecological types with shortened rhizome internodes and diversified flower and fruit coloration in response to different altitudes. Studying the genetic mechanisms of different ecotype germplasm is significant for guiding variety breeding in different cultivation areas. MethodsThe bHLH gene family was identified based on the whole-genome datasets of G. elata f. elata and G. elata f. glauca. Subsequently， the gene family members were subject to analysis， including gene structure， chromosomal localization， cis-acting elements， gene synteny， and phylogeny. Combined with transcriptome data and quantitative Real-time PCR， the expression patterns of bHLH genes in the stems of the different G. elata ecotype germplasm were analyzed. Finally， correlation analysis was conducted between gene expression patterns and color to obtain the key bHLH genes regulating the color formation of stem. ResultsA total of 63 bHLH genes were identified in both G elata f. elata and G. elata f. glauca， unevenly distributed across 17 chromosomes and clustered into 16 subfamilies， with significant expansion in some family members. Obvious inversions of bHLH genes on the same chromosome and interchromosomal translocations were detected in the two ecotype germplasm. Among these genes， 12 bHLH genes （such as bHLH62-3 and bHLH74） were associated with the bright yellow color of G elata f. elata stem， while 9 bHLH genes （such as PIL13， UNE12， and bHLH130） were correlated with the red color of G. elata f. glauca stem. Compared to G. elata f. glauca， the bHLH48 expression level was significantly higher in flowers and scale leaves of G elata f. elata， and the bHLH62-3 expression level was significantly higher in all organs of G elata f. elata. ConclusionsFunctional pathway divergence of the bHLH family members has occurred across different chromosomes in G elata f. elata and G. elata f. glauca. Through synergism or antagonism with other genes， 21 bHLH genes participate in the coloration metabolic pathway regulation of stems， flowers， and fruits. Specifically， bHLH62-3 is involved in regulating stem color differentiation in the anthocyanin biosynthesis pathway of G. elata， thus relevant to the color formation of stem. Additionally， GebHLH48 positively regulates flowering-related pathways to promote the early-flowering phenotype of G. elata f. elata. These findings have laid the foundation for analyzing the genetic regulatory mechanisms underlying the color formation of the G. elata stem.

Chronic obstructive pulmonary disease (COPD) is the most common chronic respiratory disease around the world, and pharmacotherapy is the foremost treatment method currently. In recent decades, with the rapid development of bronchoscopic interventional therapy, endoscopic physical ablation technology presents a therapeutic effect in treating COPD, with few treatment-related side effects, showing excellent application prospects in treating COPD. Since ablation techniques in this field are emerging technologies with low patient acceptance, they are not widely used in the clinical treatment of COPD. This article reviews the development process of physical ablation techniques. Moreover, their current application status and the prospects in the field of COPD treatment are also summarized and analyzed. We hope to promote the application of physical ablation in the clinical treatment of COPD and provide practical references and a theoretical basis for the clinical treatment of COPD.

Microorganisms are closely associated with human health, and their pathogenicity is a key factor in various infectious diseases, particularly in dentistry, where they contribute to common conditions such as dental caries, periodontitis, and oral mucosal diseases. Accurate and rapid microbial detection is crucial for early diagnosis, targeted therapy, and disease prevention. Conventional methods, including bacterial culture and molecular biological assays, offer specificity but are limited by long detection cycles, complex procedures, and dependence on laboratory conditions. Terahertz (THz) spectroscopy has emerged as a promising tool in microbial detection due to its non-ionizing nature, high sensitivity, and specific responses to water molecules and biomacromolecules. Integrating THz time-domain spectroscopy, near-field imaging, and metamaterial-enhanced techniques, studies have demonstrated the ability of this approach to effectively distinguish bacteria, fungi, and yeast, differentiate gram-positive and gram-negative bacteria, and even assess bacterial viability. Machine learning has further enhanced feature extraction and classification accuracy, and THz-based methods have shown notable advantages in multi-class microbial identification, detection of antibiotic-resistant strains, and quantitative analysis of microbial concentrations. However, current THz technologies are still constrained by strong water absorption, limited penetration depth, and the lack of standardized spectral databases. Future efforts should focus on mitigating water background interference, improving detection in complex samples, and establishing unified microbial spectral standards. This review systematically summarizes the latest advances of THz technologies in microbial detection, analyzes their mechanisms, advantages, and translational challenges, and proposes directions for future research.

Objective To explore the predictive value of peripheral blood cells in the efficacy of neoadjuvant immunotherapy combined with chemotherapy for esophageal squamous cell carcinoma. Methods A retrospective study was conducted on patients with esophageal squamous cell carcinoma (clinical stages Ⅱ-Ⅳa) who underwent neoadjuvant immunotherapy combined with chemotherapy at the Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College from April 2020 to November 2023. According to whether the pathology was completely relieved after treatment, patients were divided into a pathological complete remission group and a pathological incomplete remission group. The College of American Pathologists criteria were used to evaluate the tumor pathological regression grade (TRG) after neoadjuvant therapy (TRG=0, 1 defined as a good efficacy group, TRG=2, 3 defined as a poor efficacy group). Results A total of 92 patients with esophageal squamous cell carcinoma were collected, including 72 males and 20 females. The average age was (65.86±7.66) years. The complete remission of pathology was closely related to the number of lymphocytes in the blood before treatment (P=0.019). The area under the curve (AUC) for predicting complete remission of esophageal squamous cell carcinoma after neoadjuvant immunotherapy combined with chemotherapy was 0.678, the maximum Youden index was 0.328, and the optimal cutoff value was 1.845. The incidence of postoperative pulmonary infection in the pathological incomplete remission group was higher than that in the pathological complete remission group (25.0% vs. 5.6%, P=0.030). Using the optimal cutoff value, there were statistically significant differences in pathological N stage and pathological TNM stage between patients with lymphocyte counts <1.845×109/L and ≥1.845×109/L (P<0.05). Treatment response (by TRG) was significantly associated with the pretreatment red blood cell count (P=0.009). The AUC for predicting a good TRG response was 0.669, with a maximum Youden index of 0.385 and an optimal cutoff value of 4.235. Between the good and poor response groups, there were statistically significant differences in postoperative pathological T stage (P<0.001), N stage (P=0.041), and TNM stage (P＜0.001). When stratified by the optimal cutoff value, there were statistically significant differences in age (P＜0.001) and the prevalence of hypertension (P=0.022) between patients with red blood cell counts <4.235×1012/L and ≥4.235×1012/L. Conclusion A pretreatment absolute lymphocyte count ≥1.845×109/L and a red blood cell count <4.235×1012/L are good predictors for pathological complete response and a good pathological response, respectively, following neoadjuvant immunotherapy combined with chemotherapy in patients with esophageal squamous cell carcinoma.

Objective To understand mosquito density, species composition, and seasonal variations around the Beijing Capital International Airport(BCIA)and provide scientific evidence for mosquito-borne disease prevention and control. Methods Meteorological data were collected from 2016 to 2022, and mosquito density was monitored at seven surveillance sites around the BCIA from May to October each year using CO2-baited mosquito traps to analyze the relationship between mosquito density and meteorological factors. Results In total,68 518 female mosquitoes were captured, with a mosquito density of 10.20 per light · hour. The dominant species was Culex pipiens pallens(73.90%), followed by Aedes albopictus(9.71%)and Ae. vexans(9.29%);the mosquito density was highest in 2016(14.8 per light·hour)and lowest in 2022(4.46 per light·hour). Significant statistical differences were observed in mosquito density among different species(F=18.118, P<0.05). The density of Cx. pipiens pallens and other mosquito species showed a mutually exclusive trend; the peak of mosquito density varied based on years and habitats, with significant statistical differences in mosquito density among different habitats(F=8.504, P<0.05). The highest mosquito density was observed near the Wenyu River(21.50 per light·hour), whereas the lowest was recorded at the BCIA construction site(2.44 per light· hour). The monthly average temperature(r=0.595), monthly average highest temperature(r=0.575), and monthly average lowest temperature(r=0.624)showed moderate positive correlations, whereas the monthly average air pressure(r=-0.484)showed a moderate negative correlation. The monthly average minimum temperature was included in the regression model(F=25.575, P<0.000), and the equation Y=1.029X4-8.181 was used. The monthly average air pressure 2 months prior(b=-2.418, β=-1.619, P<0.05)and the monthly average relative humidity(b=-0.739, β=-1.201, P<0.05)significantly negatively predicted the mosquito density. Notably, the regression equation used was y=2526.170+(-2.418)X9+(-0.739)X13. The exposure-response analysis revealed that the density of mosquitoes was not linearly related to the average monthly air pressure, rainfall, and duration of sunshine. Conclusions The dominant mosquito species around the BCIA was Cx. pipiens pallens, Ae. albopictus, and Ae. vexans. During the control of Cx. pipiens pallens, the impact on other mosquito species must also be considered. Environmental management, breeding sites, and scientific use of pesticides should be prioritized based on the activity periods of mosquitoes. Further studies on mosquitoes and meteorological factors should be conducted to provide references and novel avenues for mosquito control.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Objective: To investigate the trend in incidence and change in age at onset of malignant tumor in cancer registration areas of Jiangsu Province from 2009 to 2021, so as to provide the evidence for formulating cancer prevention and control strategies and optimizing the allocation of healthcare resources. Methods: Incidence data of malignant tumor cases from 2009 to 2021 were collected from the aggregated database of 16 qualified cancer registries of Jiangsu Province. The crude incidence, age-specific incidence, average age at onset, proportion of age-specific incidence, and proportion of incidence in cases aged ≥60 years were calculated by genders and urban/rural areas, and age-standardized using the Segi's world standard population. The trend in incidence of malignant tumor from 2009 to 2021 was evaluated using average annual percent change (AAPC). The trend in average age at onset of malignant tumor from 2009 to 2021 was evaluated using the linear regression model. Results: From 2009 to 2021, a total of 703 185 cases of malignant tumor were reported in Jiangsu Province, comprising 400 970 males and 302 215 females. The crude incidence of malignant tumor increased from 268.26/100 000 in 2009 to 380.97/100 000 in 2021 (AAPC=2.880%, P<0.05). From 2009 to 2021, the world population-standardized incidence of malignant tumor showed upward trends in the total population, females, and urban and rural areas (AAPC=0.635%, 2.332%, 0.795%, and 0.385%, all P<0.05), while a downward trend was observed in males (AAPC=-0.608%, P<0.05). From 2009 to 2021, the crude incidence of malignant tumor in the groups aged 0-<30 years, 30-<40 years, 40-<50 years, 60-<70 years, and ≥80 years showed upward trends (AAPC=3.160%, 4.462%, 1.295%, 0.569%, and 1.496%, all P<0.05), a downward trend was found in the group aged 50-<60 years (AAPC=-0.860%, P<0.05), while no statistically significant trend was observed in the group aged 70-<80 years (P>0.05). The world population-standardized average age at onset showed downward trends in the total population, females, and urban areas, with average annual decreases of 0.085, 0.223, and 0.136 years, respectively (all P<0.05). Conversely, an upward trend was observed in males, with an average annual increase of 0.081 years (P<0.05). No statistically significant trend was found in rural areas (P>0.05). Compared with 2009, the proportion of malignant tumor incidence cases increased in all age groups between 20-<50 years in 2021. Additionally, the proportion of malignant tumor cases aged over 60 years showed a downward trend from 2009 to 2021 (AAPC=-0.322%, P<0.05). Conclusions From 2009 to 2021, the overall incidence of malignant tumor in registration areas of Jiangsu Province showed an upward trend, with the age at onset tending to become younger. There were differences in the incidence trends across genders and urban/rural areas.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

BACKGROUND: Prolonged occupational noise exposure poses potential health risks, but its impact on mitochondrial DNA (mtDNA) damage and methylation patterns remains unclear. METHOD: We recruited 306 factory workers, using average binaural high-frequency hearing thresholds from pure-tone audiometry to assess noise exposure. MtDNA damage was evaluated through mitochondrial DNA copy number (mtDNAcn) and lesion rate, and mtDNA methylation changes were identified via pyrophosphate sequencing. RESULTS: There was a reduction in MT-RNR1 methylation of 4.52% (95% CI: -7.43% to -1.62%) among workers with abnormal hearing, whereas changes in the D-loop region were not statistically significant (β = -2.06%, 95% CI: -4.44% to 0.31%). MtDNAcn showed a negative association with MT-RNR1 methylation (β = -0.95, 95% CI: -1.23 to -0.66), while no significant link was found with D-loop methylation (β = -0.05, 95% CI: -0.58 to 0.48). Mediation analysis indicated a significant increase in mtDNAcn by 10.75 units (95% CI: 3.00 to 21.26) in those with abnormal hearing, with MT-RNR1 methylation mediating 35.9% of this effect. CONCLUSIONS These findings suggest that occupational noise exposure may influence compensatory increases in mtDNA content through altered MT-RNR1 methylation.
Humans ; DNA, Mitochondrial ; DNA Methylation ; Male ; Adult ; Noise, Occupational/adverse effects* ; Middle Aged ; Occupational Exposure/adverse effects* ; Biomarkers/blood* ; Female