Objective To explore the wearing status and actual noise reduction effect of hearing protectors among noise workers in a typical automobile manufacturing enterprise. Methods In April 2024, an occupational hazard factor testing was carried out in an automobile manufacturing industry, and at the same time, the hearing protection fit test was conducted for noise workers. Intervention and guidance were provided to those who did not pass the minimum standard of baseline PAR. The difference in PAR between baseline and post-intervention was compared, and the effectiveness of hearing protector wearing method training was evaluated. Results The exceeding rate of the company's noise operation post was 50.77% (66/130). The baseline PAR of the subjects with working experience of less than 15 years and wearing hearing protectors throughout noisy work was higher, and the differences were statistically significant (P<0.05). Compared with those with 80dB≤L_{EX, 8h}<85dB, more research subjects with L_{EX, 8h}≥85dB failed baseline PAR (39.13%). After intervention, the PAR of the subjects who did not pass the minimum standard of baseline PRA increased from 2.0 (0.0, 5.3) to 17.0 (14.8, 20.0), and the protection level was significantly improved, and the difference was statistically significant (P<0.01). Conclusion The individual fit test of hearing protector is an important means to evaluate the actual noise reduction level of hearing protector and guide the selection of hearing protection models. Corporate training can help improve the PAR of hearing protectors.

ObjectiveTo explore the effect of piercing electroacupuncture on ankle muscle strength, dynamic balance and subjective stability in patients with functional ankle instability (FAI). MethodsA total of 48 patients with FAI admitted to the Affiliated Hospital of Traditional Chinese Medicine, Shandong Second Medical University from April, 2024 to July, 2025 were enrolled and randomly divided into control group (n = 24) and experimental group (n = 24). The control group received multimodal functional training, while the experimental group received piercing electroacupuncture in addition, for six weeks. Relative peak torque of ankle plantarflexion, dorsiflexion, inversion and eversion were measured with isokinetic muscle strength testing system before and after treatment, while Y-Balance Test (YBT) and Cumberland Ankle Instability Tool (CAIT) were adopted for evaluation. ResultsOne case dropped out in each group. Relative peak torque of ankle plantarflexion, dorsiflexion, inversion and eversion at each angular velocity, the normalized distance in each direction and composite score of YBT, as well as the score of CAIT increased in both groups after treatment (|t| > 2.891, P < 0.01), while all the indicators were better in the experimental group than in the control group (|t| > 2.104, P < 0.05), except dorsiflexion muscle strength. ConclusionCombination of piercing electroacupuncture can further improve perimalleolar muscle strength, dynamic balance and subjective stability in patients with FAI.

Traditional treatment for type 1 diabetes mellitus （T1DM） primarily involves insulin replacement， yet some patients encounter issues such as significant blood glucose fluctuations， high risk of hypoglycemia， and weight gain. In recent years， the adjuvant therapeutic role of non-insulin hypoglycemic drugs in T1DM has gradually gained attention. This article reviews the mechanisms of action and clinical research progress of five types of non-insulin hypoglycemic drugs in the treatment of T1DM： amylin analogues （pramlintide）， biguanides （metformin）， sodium-glucose co-transporter 2 inhibitor， dipeptidyl peptidase-4 inhibitor， and glucagon-like peptide-1 receptor agonist. It is found that these drugs can enhance clinical benefits for T1DM patients by improving insulin sensitivity， delaying gastric emptying， promoting urinary glucose excretion， and regulating incretin levels， thereby reducing glycated hemoglobin levels， decreasing insulin dosage， and managing body weight. Simultaneously， these drugs also present limitations such as low patient compliance due to complex dosing regimens， increased risk of diabetic ketoacidosis， and heterogeneity in glycemic control. Future research could focus on developing individualized treatment strategies， combining pharmacogenomics with novel biomarkers to precisely identify subpopulations of patients who may benefit， and delving into the potential value of these drugs in delaying diabetic vascular complications and improving patients’ quality of life.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To explore the predictive value of artificial intelligence (AI)-based lung nodule CT quantitative analysis for the invasion degree of lung adenocarcinoma spectrum lesions. Methods According to the invasion degree of lung adenocarcinoma spectrum lesions, patients with surgically and pathologically confirmed lung adenocarcinoma spectrum lesions from January to June 2023 in Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University were retrospectively collected and divided into a non-invasive group and an invasive group, including atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma patients in the non-invasive group, and invasive adenocarcinoma patients in the invasive group. All enrolled patients underwent chest CT before surgery, and then the lung nodules were quantitatively analyzed using an AI-based computer-aided diagnosis system to compare the related quantitative parameters of lung nodules that have been surgically removed and pathologically confirmed as lung adenocarcinoma spectrum lesions between the two groups. The relationship between various CT quantitative features and the invasion degree of lung adenocarcinoma spectrum lesions was analyzed. Results A total of 149 patients (149 lesions) were included, including 42 males and 107 females, aged 29-81 (56.35±10.75) years. There were 72 patients in the non-invasive group and 77 patients in the invasive group. Statistical differences were observed between the two groups in long diameter, short diameter, volume, surface area, mass, maximum cross-sectional area, 3D long diameter, maximum CT value, minimum CT value, average CT value, entropy, kurtosis, skewness, malignancy probability and other indicators (P<0.05). Multivariate binary logistic regression analysis showed that long diameter [OR=1.687, 95%CI (1.364, 2.085), P<0.001], average CT value [OR=1.006, 95%CI (1.002, 1.009), P=0.002], and malignancy probability [OR=1.034, 95%CI (1.005, 1.063), P=0.020] were independent risk factors for the invasion degree of lung adenocarcinoma. The predictive model combining the above parameters demonstrated optimal performance, with an area under the receiver operating characteristic curve of 0.951, sensitivity of 0.818, and specificity of 0.972. Using a Nomogram to quantify the three independent risk factors, the cross-validation was performed to evaluate the stability of the model, and the average C-index of cross-validation was 0.950, with each fold C-index >0.75, indicating that the prediction performance of the model was stable, and the calibration curve and decision curve indicated good predictive performance. Conclusion The visualization prediction model constructed by AI-based quantitative analysis of lung nodules in CT demonstrates significant discriminative effectiveness in the assessment of invasiveness in lung adenocarcinoma spectrum lesions. This visualization prediction model can provide a quantitative decision-making basis for the preoperative identification of the degree of invasiveness in lung adenocarcinoma spectrum lesions.

Objective To analyze the increase in hearing thresholds in different frequency bands of workers exposed to different characteristic noises and the influencing factors of binaural high-frequency hearing loss, and to provide theoretical support for occupational health monitoring in enterprises. Methods In 2021, noise-exposed workers were selected from typical noise-exposing enterprises as research subjects, and their occupational health examination data were tracked for three consecutive years. According to the on-site detection from 2021 to 2023, the cumulative noise exposure (CNE) was calculated. The chi-square test was used to compare the increase of the hearing threshold of workers exposed to different characteristic noises in different frequency bands. A generalized estimation equation was constructed to explore the influencing factors of high-frequency hearing loss in the research subjects. Results The age of the study subjects was 23-54 years old, with 1.6-20.1 years of working experience, mainly distributed in manufacturing workshops, technical workshops and public works workshops. The median LAeq,8h was 85.90 (84.5, 86.1) dB(A), and the average CNE was 94.3±2.6 dB(A). The number of people with increased hearing thresholds in each frequency band was 0.5kHz>6kHz>4kHz>3kHz>1kHz>2kHz. There were statistically significant differences in the increase of hearing thresholds in each frequency band, binaural speech frequency impairment, and high-frequency hearing loss among subjects with different characteristics (P<0.05). The detection rate of binaural speech frequency hearing loss was the highest in subjects aged>45 years, with a length of service of>15 years, and with a CNE>96dB(A). The detection rate of binaural high-frequency hearing loss was the highest in subjects aged 35-40 years, with a length of service of 5-10 years, and with a CNE>96dB(A).In addition, the detection rate of binaural speech frequency and high-frequency hearing loss increased with the increase of CNE(P<0.05). The GEE model showed that age>40 years and CNE>96dB(A) were risk factors for high-frequency hearing loss in both ears. Conclusion With increasing age and CNE, hearing thresholds rise across all frequency bands, elevating the risk of binaural speech-frequency and high-frequency hearing loss, thereby increasing the likelihood of occupational noise-induced deafness.

Objective To examine the impact of climate, land use and their interaction effect on the population density of Oncomelania hupensis. Methods O. hupensis snail surveillance data in Yunnan Province from 2013 to 2022 were obtained from the National Information System for Parasitic Diseases Prevention and Control of Chinese Information System for Disease Control and Prevention, and the density of living snails was calculated in each county-level administrative district. Climate data were obtained from the China Meteorological Forcing Dataset version 2.0, and land use data were captured from the China Land Cover Dataset. The raster climate data and land use data were clipped to the administrative border of Yunnan Province in the R package version 4.5, and the climate data were resampled to a spatial resolution of 30 m × 30 m consistent with the land use data using bilinear interpolation. Annual meteorological indicators were extracted from each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022, including average temperature, maximum temperature, minimum temperature, temperature difference, average precipitation rate, maximum precipitation rate, minimum precipitation rate, downward shortwave radiation flux, downward longwave radiation flux, air pressure, wind speed, specific humidity and relative humidity. Annual land use structure and landscape pattern index in each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022 were extracted and calculated, including the proportion of areas occupied by each type of land use, patch density, largest patch index, landscape separation index, landscape fragmentation index, landscape segmentation index, contagion index, patch cohesion index, edge density, and shape index. Land use entropy index was introduced to represent the degree of habitat fragmentation and ecological niche breadth. The density of living snails and environmental factors were subjected to rank correlation analysis, and variables with no statistically significant correlation (P>0.1) were excluded. Then, a correlation coefficient matrix was constructed, and |r_s| of 0.85 and greater was defined as the threshold of high collinearity. For highly correlated variables in each group, indicators with a more biological explanatory power or a better statistical efficiency were retained. A generalized additive model (GAM) was constructed with O. hupensis snail density as a response variable and screened environmental variables as independent variables, and the model performance was evaluated using the variance explained. Results The area occupied by O. hupensis snail habitats was 1 056 to 1 680 hm² in Yunnan Province from 2013 to 2022, appearing an overall tendency towards a decline; however, the density of living snails appeared a tendency towards a slight rise. Based on the correlation coefficient matrix, 14 core explanatory variables were retained, including farmland, water bodies, bare land, impervious surface, landscape separation index, landscape fragmentation index, edge density, shape index, land use entropy index, downward longwave radiation flux, downward shortwave radiation flux, maximum temperature, minimum precipitation rate and specific humidity. Two types of GAM were constructed, including the main-effects model without interaction terms and the interaction model with interaction terms. The corrected Akaike information criterion (AICc) of the optimal main-effects model was −285.223, and the core variables included bare land, impervious surface, landscape separation index, edge density, shape index, land use entropy index and downward shortwave radiation flux. Following introduction of second-order interaction terms, the AICc value of the optimal interaction model was −345.526, and the significant interaction combinations included: farmland × impervious surface, farmland × landscape separation index, farmland × shape index, farmland × land use entropy index, impervious surface × landscape separation index, impervious surface × edge density, and shape index × downward shortwave radiation flux. The proportion of the main-effects model explaining the variation of O. hupensis snail density was 39.8%, and the explanatory power of GAM increased to 79.0% following introduction of interaction terms. Conclusion Landscape separation index, edge density, shape index and other landscape pattern indicators pose significant impacts on the intensity and direction of changes in O. hupensis snail density in Yunnan Province.

ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Objective To investigate the preventive effects of lidocaine administered through different routes on cardiovascular stress responses during anesthesia tracheal intubation. Methods Total 120 patients scheduled for elective surgery under general anesthesia were randomly divided into three groups: intravenous injection group （group IV）, throat spray group （group LJ）, and control group （group CT）, with 40 patients in each. Group IV received 50 mg of lidocaine via intravenous injection 1 minute before tracheal intubation. Group LJ received 50 mg of lidocaine sprayed into the pharyngeal cavity, glottis, and subglottic area. Group CT did not receive any treatment, and the remaining procedures were performed following the routine general anesthesia induction protocol. Heart rate （HR）, systolic blood pressure （SBP）, diastolic blood pressure （DBP）, and mean arterial pressure （MAP） were recorded at four time points: T₀ （before tracheal intubation）, T₁ （immediately after tracheal intubation）, T₂ （3 minutes after intubation）, and T₃ （5 minutes after intubation）. Statistical analysis of the data was performed using SPSS 22.0. Results There were no significant differences in HR at various time points within the group LJ. The changes in HR in the group IV and group CT were different statistically from those in the throat spray group. The blood pressure of patients in all three groups increased to varying degrees immediately after tracheal intubation, with the group CT showing particularly significant changes that differed significantly from both the group IV and the group LJ. The group LJ rapidly returned to levels close to those before intubation. Conclusion The preventive effects of lidocaine on stress responses during tracheal intubation were different depending on the route of administration. The inhibitory preventive effect of the throat spray method was superior to that of intravenous lidocaine, especially in preventing changes in heart rate.