Objective:To analyze the current status and trends of lung cancer incidence and mortality in China and selected global regions, providing evidence for lung cancer prevention strategies in China.Methods:We extracted data from the GLOBOCAN 2022 database. Age-standardized Incidence rate (ASIR) and Age-standardized Mortality rate (ASMR) were calculated using Segi's world standard population. Epidemiological patterns were analyzed by region, age, sex, and human development index (HDI). Simple linear regression and Spearman's rank correlation coefficient were used to examine associations between HDI and ASIR/ASMR.Results:In 2022, global lung cancer incidence and mortality reached 2.48 million and 1.82 million cases respectively, with age-standardized rates of 23.6 per 100 000 (ASIR) and 16.8 per 100 000 (ASMR). Gender disparities were prominent, with male ASIR and ASMR being 2.0-fold and 2.5-fold higher than females. Elderly populations showed 11.6-fold higher ASIR and 14.4-fold higher ASMR compared to working-age adults. HDI demonstrated strong positive correlations with both ASIR ( r=0.79, P＜0.001) and ASMR ( r=0.74, P＜0.001). China accounted for 1.06 million new cases and 0.73 million deaths, with ASIR (40.8 per 100 000) and ASMR (26.7 per 100 000) exceeding global averages by 1.7-fold and 1.6-fold respectively. Chinese males showed 1.7-fold higher ASIR and 2.7-fold higher ASMR than females. Trend analysis revealed persistently high male incidence in China whereas rapidly increasing female rates, narrowing gender disparities. Projections estimate 1.80 million incident cases and 1.41 million deaths by 2050, representing 69.3% and 92.0% increases from 2022 levels. Conclusions:Significant heterogeneity exists in lung cancer burden across demographics and development levels, with strong HDI correlations. China bears disproportionate disease burden, necessitating intensified prevention efforts. These findings underscore the urgency of targeted interventions in high-risk populations.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To analyze the current status and trends of lung cancer incidence and mortality in China and selected global regions, providing evidence for lung cancer prevention strategies in China.Methods:We extracted data from the GLOBOCAN 2022 database. Age-standardized Incidence rate (ASIR) and Age-standardized Mortality rate (ASMR) were calculated using Segi's world standard population. Epidemiological patterns were analyzed by region, age, sex, and human development index (HDI). Simple linear regression and Spearman's rank correlation coefficient were used to examine associations between HDI and ASIR/ASMR.Results:In 2022, global lung cancer incidence and mortality reached 2.48 million and 1.82 million cases respectively, with age-standardized rates of 23.6 per 100 000 (ASIR) and 16.8 per 100 000 (ASMR). Gender disparities were prominent, with male ASIR and ASMR being 2.0-fold and 2.5-fold higher than females. Elderly populations showed 11.6-fold higher ASIR and 14.4-fold higher ASMR compared to working-age adults. HDI demonstrated strong positive correlations with both ASIR ( r=0.79, P＜0.001) and ASMR ( r=0.74, P＜0.001). China accounted for 1.06 million new cases and 0.73 million deaths, with ASIR (40.8 per 100 000) and ASMR (26.7 per 100 000) exceeding global averages by 1.7-fold and 1.6-fold respectively. Chinese males showed 1.7-fold higher ASIR and 2.7-fold higher ASMR than females. Trend analysis revealed persistently high male incidence in China whereas rapidly increasing female rates, narrowing gender disparities. Projections estimate 1.80 million incident cases and 1.41 million deaths by 2050, representing 69.3% and 92.0% increases from 2022 levels. Conclusions:Significant heterogeneity exists in lung cancer burden across demographics and development levels, with strong HDI correlations. China bears disproportionate disease burden, necessitating intensified prevention efforts. These findings underscore the urgency of targeted interventions in high-risk populations.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To explore the clinical application value of a deep learning algorithm-based ultrasound biomicroscopy (UBM) image analysis system for primary angles-closure glaucoma (PACG) anterior located ciliary body.Methods:A diagnostic test study was conducted.A total of 2 132 UBM images from 726 eyes of 378 PACG patients who underwent UBM examination were collected at Renmin Hospital of Wuhan University from August 2022 to December 2023.The dataset was divided into a training set of 1 599 images and a test set of 533 images, and a deep learning algorithm was employed to construct a model.An additional 334 UBM images from 101 eyes of 69 PACG patients treated at Huangshi Aier Eye Hospital were selected to conduct external testing.A separate set of another 110 UBM images were selected for a human-machine competition to compare the accuracy and speed between anterior located ciliary body evaluation system and three senior ophthalmologists.Furthermore, eight junior ophthalmologists assessed the 110 UBM images independently without and with the assistance of the model, and the differences between the two evaluations were analyzed to assess the assisstance effect of the model.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109).Results:The model achieved an accuracy of 93.43% for anterior located ciliary body identification in the internal test set, with a sensitivity of 84.30% and a specificity of 97.78%.The model also performed well on the external test set with an accuracy of 92.81%.In the human-machine competition, the model's accuracy was comparable to that of the senior ophthalmologists and outperformed two of the three senior ophthalmologists.The average total time of the three senior ophthalmologists was 726.73 seconds, approximately 12.47 times longer than the model's 58.30 seconds.With model assistance, the diagnostic accuracy of the eight junior ophthalmologists was 86.71%, which was significantly higher than 76.25% without model assistance ( χ2=-7.550, P<0.001).And the image evaluation time was (714.91±213.82)seconds, which was significantly lower than (987.90±238.56)seconds without model assistance ( t=2.774, P<0.05). Conclusions:The UBM image analysis system based on a deep learning algorithm demonstrates high accuracy in diagnosing anterior located ciliary body in PACG and provides a strong support for the UBM image recognition training of junior ophthalmologists.

Objective:To construct an artificial intelligence (AI)-assisted system based on deep learning for corneal in vivo confocal microscopy (IVCM) image recognition and to evaluate its value in clinical applications. Methods:A diagnostic study was conducted.A total of 18 860 corneal images were collected from 331 subjects who underwent IVCM examination at Renmin Hospital of Wuhan University and Zhongnan Hospital of Wuhan University from May 2021 to September 2022.The collected images were used for model training and testing after being reviewed and classified by corneal experts.The model design included a low-quality image filtering model, a corneal image diagnosis model, and a 4-layer identification model for corneal epithelium, Bowman membrane, stroma, and endothelium, to initially determine normal and abnormal corneal images and corresponding corneal layers.A human-machine competition was conducted with another 360 database-independent IVCM images to compare the accuracy and time spent on image recognition by three senior ophthalmologists and the AI system.In addition, 8 trainees without IVCM training and with less than three years of clinical experience were selected to recognize the same 360 images without and with model assistance to analyze the effectiveness of model assistance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY2021-K148).Results:The accuracy of this diagnostic model in screening high-quality images was 0.954.Its overall accuracy in identifying normal/abnormal corneal images was 0.916 and 0.896 in the internal and external test sets, respectively.Its accuracy reached 0.983, 0.925 in the internal test sets and 0.988, 0.929 in the external test sets in identifying corneal layers of normal and abnormal images, respectively.In the human-machine competition, the overall recognition accuracy of the model was 0.878, which was similar to the average accuracy of the three senior physicians and was approximately 300 times faster than the experts in recognition speed.Trainees assisted by the system achieved an accuracy of 0.816±0.043 in identifying corneal layers of normal and abnormal images, which was significantly higher than 0.669±0.061 without model assistance ( t=6.304, P<0.001). Conclusions:A deep learning-based assistant system for corneal IVCM image recognition is successfully constructed.This system can discriminate normal/abnormal corneal images and diagnose the corresponding corneal layer of the images, which can improve the efficiency of clinical diagnosis and assist doctors in training and learning.

Objective: To compare the effectiveness of qualitative and quantitative fecal immunochemical tests (FIT) in identifying colorectal cancer, so as to provide insights into perfecting screening strategies for colorectal cancer. Methods: Participants in the Colorectal Cancer Screening Program for Key Populations in Zhejiang Province from May 2020 to December 2021 were recruited, and their demographic information, lifestyle and disease history were collected through a questionnaire survey. Qualitative or quantitative FIT along with a questionnaire-based risk assessment were employed as the initial screening tests. Individuals who were positive in any FIT or had high-risk assessment results were required to attend a subsequent colonoscopy examination. The positive rate, detection rate of colorectal cancer, positive predictive value and number of colonoscopies required were compared between qualitative and quantitative FITs, and stratified analyses by gender and age were conducted. Results: Totally 4 099 769 participants were included. The qualitative FIT group included 3 574 917 individuals, yielding a positive rate of 11.35%, a detection rate of 1.19%, a positive predictive value of 0.48% and 83.84 colonoscopies required to detect one cancer case. The quantitative FIT group involved 524 852 individuals, yielding a positive rate of 6.70%, a detection rate of 2.31%, a positive predictive value of 1.01% and 43.23 colonoscopies required to detect one cancer case. The quantitative FIT group showed significantly higher detection rate of colorectal cancer, higher positive predictive value and less number of colonoscopies required compared to the qualitative FIT group (all P<0.05). The same results were obtained after stratification by gender and age. Conclusion Compared to qualitative FIT, quantitative FIT improves the detection of colorectal cancer and reduces the workload of colonoscopy examinations, making it more suitable for colorectal cancer screening in large-scale populations.

Objective:To investigate the clinical, skeletal muscle pathological, and genetic characteristics of fatal infantile hypertonic myofibrillar myopathy (FIHMM).Methods:The clinical manifestations, laboratory assessments data and gene sequencing results of 10 patients diagnosed with FIHMM in Shenzhen Children′s Hospital from February 2017 to April 2021 were retrospectively analyzed.Magnetic resonance imaging (MRI) of both musculoskeletal system and the brain, and electromyogram (EMG) were performed in 3 cases, while muscle biopsy was performed in 2 cases.Results:Among these 10 cases, 1 case was from Northeast China and 1 case from East China, while the rest 8 cases were from South China.Eight of the 10 patients were male, and the other 2 cases were female.They were all born normal and not related to each other.The age of onset varied from 2 to 12 months.The main clinical manifestations for all the patients were progressive rigidity of the rectus abdominis (8 cases), neck muscles (7 cases), rectus abdominis (2 cases) and intercostal muscles (1 case), resulting in respiratory failure.Mildly to moderately elevated serum creatine kinase level was detected (436-5 804 IU/L) (reference range: 24-229 IU/L). Complex repetitive discharges can be seen in the EMG, without any myotonic potential.Muscle fiber degeneration, necrosis, and vacuolar degeneration were noted in the histopathological examination of the vastus lateralis and rectus abdominis.An abnormal red granular deposit was observed in a portion of the field of the modified Gomory Trichrome staining.Immunohistochemistry showed substantial deposition of desmin.Under the electron microscopy, the sarcomere structure of the muscle fibers was seriously disordered, with the destruction of Z-bands and the presence of granular deposits.The whole-exome sequencing identified the same homozygous variation c. 3G>A, p.Met1? of CRYAB gene in all the patients, but heterozygous variation in their parents. Conclusions:Axial muscles involvement, such as rectus abdominis rigidity, is the main clinical characteristic of FIHMM.c.3G>A, p.Met1? mutation in the CRYAB gene is a hotspot mutation in Chinese children.

Objective:To explore the application value of case-based learning (CBL) and problem-based learning (PBL) dual-track teaching mode in the standardized resident training in laboratory medicine.Methods:The students of Grade 2017 and Grade 2018, who underwent standardized resident training of laboratory medicine in the Second Affiliated Hospital of Wenzhou Medical University from September 2017 to June 2021, were selected in this study. Seven students of Grade 2017 were served as the traditional lecture teaching group (traditional teaching group), and 12 students of Grade 2018 were assigned to the CBL-PBL dual-track teaching mode group (CBL-PBL group). Students of the two groups received 22 months of laboratory specialty training, and underwent one admission theory assessment,two mid-term theory assessments, annual professional level test, final theory assessment, final practical skills assessment as well as questionnaire survey. The questionnaire was distributed and finished anonymously after the final assessment. Survey indicators included ability assessment on solving clinical problem, assessment on the theoretical knowledge, computer operation skill, preparation time before teaching, teaching method satisfaction degree and clinical recognition. The results were divided into 5 categories: extremely agree, agree, general, disagree and extremely disagree, respectively (ranked as 5, 4, 3, 2, and 1). The Cronbach′s α coefficient was used to analyze the reliability of the questionnaire.Results:The mid-term theory assessment, annual professional level test and assessment results of clinical outcome interpretation were significantly higher in CBL-PBL dual-track teaching mode group than those in traditional lecture teaching group (all P<0.05). The performance after standardized resident training was significantly higher in the CBL-PBL group than in the traditional teaching group ( P<0.05). The Cronbach′s α coefficient of questionnaire reliability analysis was 0.938. Parameters assessment results including improved clinical problem-solving skills, computer operation skill, the ability to analyze and solve problems, the ability of innovation and adaptation, the learning interest, the ability of autonomous learning, teaching method satisfaction and conventional application to clinical recognition scores were all significantly better in the CBL-PBL group than those in the traditional teaching group (all P<0.05). Conclusion:The application of CBL-PBL dual-track teaching mode for the standardized resident training in laboratory medicine can effectively improve the assessment results and performance of students undergoing standardized resident training, and help to cultivate high quality medical laboratory professionals.

Implant dentures have become the main method for the treatment of dentition defects or complete edentulism. However, due to the lack of periodontal ligament and periodontal ligament proprioceptors, implant dentures have very limited cushioning and sensing capabilities and are prone to occlusal overload. As a risk factor for peri-implantitis, occlusal overload seriously threatens the stability and success rate of implant dentures. This paper reviews the occlusal overload of implant dentures, the causal relationship between occlusal overload and plaque biofilms in peri-implantitis, the mechanism by which occlusal overload promotes peri-implantitis, and the effect of reasonable clinical occlusal adjustment on healing. This review shows that occlusal overload is closely related to the occurrence of peri-implantitis. Occlusal overload can promote the process of peri-implantitis by increasing the release of inflammatory factors and mechanical transduction mechanisms. The intervention of the patients’ bad bite habits and occlusal adjustment can promote the healing of peri-implantitis. At present, there is no uniform standard ideal experimental model for occlusal overload. The phenomenon and mechanism of bone resorption around the implant caused by overload force still need further observation and research, which will help determine the intensity, direction and timing of occlusal loading to guide clinical occlusal adjustment.