Objective:To investigate the prevalence of skeletal fluorosis and the management and treatment of patients in endemic fluorosis areas of Shaanxi Province.Methods:From March to November 2023, in accordance with the requirements of the "2023 Monitoring Plan for Endemic Fluorosis in Shaanxi Province", the implementation of prevention and control measures of endemic fluorosis in Shaanxi Province was investigated. The data of patients with skeletal fluorosis from January to December 2023 were downloaded from Shaanxi Province Endemic Disease Control Information Management Platform, and the epidemiological characteristics were analyzed. At the same time, the management and treatment of patients with skeletal fluorosis in endemic fluorosis areas of Shaanxi Province were carried out in accordance with the requirements of the "Shaanxi Province Endemic Disease Patient Management Service Specification".Results:In 2023, there were 10 drinking water-borne endemic fluorosis cities in Shaanxi Province, involving 3 715 endemic villages, including 3 650 water improvement villages with a water-improving rate of 98.25%. There were 2 coal-burning-borne endemic fluorosis cities in Shaanxi Province, involving 641 746 households in 1 414 endemic villages. Among them, 641 617 households had changed their furnaces and stoves, with a furnace and stove change rate of 99.98%. There were 37 462 patients with skeletal fluorosis in the province, with 35 792 from drinking water-borne endemic fluorosis areas and 1 670 from coal-burning-borne endemic fluorosis areas. The condition was mainly mild and moderate (94.12%, 35 258/37 462), with females accounting for 55.13% (20 654/37 462). The education level was mainly primary school and illiteracy (83.64%, 31 333/37 462), and the occupation was mainly farmers (99.29%, 37 196/37 462). A total of 37 116 patients with skeletal fluorosis were followed up and managed, with a management rate of 99.08% (37 116/37 462). A total of 35 756 patients were managed in a standardized manner, and the standardized management rate was 96.34% (35 756/37 116). A total of 30 649 patients with skeletal fluorosis were actually treated, with a treatment rate of 84.27% (30 649/36 370) and a total effective rate of 98.08% (30 062/30 649).Conclusion:In 2023, the prevalence of skeletal fluorosis in endemic fluorosis areas of Shaanxi Province is mainly mild and moderate, with a wide coverage of community management and a high level of treatment efficiency.

Dysregulated RNA splicing is a well-recognized characteristic of colorectal cancer (CRC); however, its intricacies remain obscure, partly due to challenges in profiling full-length transcript variants at the single-cell level. Here, we employ high-depth long-read scRNA-seq to define the full-length transcriptome of colorectal epithelial cells in 12 CRC patients, revealing extensive isoform diversities and splicing alterations. Cancer cells exhibited increased transcript complexity, with widespread 3'-UTR shortening and reduced intron retention. Distinct splicing regulation patterns were observed between intrinsic-consensus molecular subtypes (iCMS), with iCMS3 displaying even higher splicing factor activities and more pronounced 3'-UTR shortening. Furthermore, we revealed substantial shifts in isoform usage that result in alterations of protein sequences from the same gene with distinct carcinogenic effects during tumorigenesis of CRC. Allele-specific expression analysis revealed dominant mutant allele expression in key oncogenes and tumor suppressors. Moreover, mutated PPIG was linked to widespread splicing dysregulation, and functional validation experiments confirmed its critical role in modulating RNA splicing and tumor-associated processes. Our findings highlight the transcriptomic plasticity in CRC and suggest novel candidate targets for splicing-based therapeutic strategies.
Humans ; Colorectal Neoplasms/metabolism* ; RNA Isoforms/metabolism* ; Single-Cell Analysis ; RNA Splicing ; Gene Expression Regulation, Neoplastic ; RNA, Neoplasm/metabolism* ; Transcriptome

Objective:To propose a deep learning（DL）-based ultrasound imaging auxiliary tool for automatic segmentation and recognition of the brachial plexus（BP），and to enhance the accuracy and safety of clinical procedures.Methods:It was a multicenter study that collected 773 healthy subjects from Peking University Third Hospital and its branch campuses，the Third Medical Center of the Chinese PLA General Hospital，and Shanghai Eighth People's Hospital between August 2024 and February 2025. Brachial plexus（BP）images in the interscalene groove were captured used high-frequency ultrasound by senior sonographers，a dataset comprising 1 289 standardized images were constructed and the improved model（CHA-TransUNet）was trained. The test set was input into 6 different models（CHA-TransUNet，R50-Unet，TransUnet，SegFormer，SwinUnet，MISSFormer）for segmentation. Segmentation accuracy was evaluated using metrics including the Dice similarity coefficient（DSC），95% Hausdorff distance（HD95）and mean intersection over union（mIoU），and was compared with the segmentation results of 3 ultrasound physicians with varying experience levels（junior physicians and senior physicians）to validate the model's segmentation efficacy.Results:The CHA-TransUNet model established based on a dataset of 1 289 standardized images achieved segmentation results for the BP with a DSC of 90.15%，mIoU of 91.02%，and HD95 of 8.08. Its accuracy was higher than other mainstream models（DSC：90.15% vs. 87.60%，87.77%，81.35%，84.78%，84.55%），significantly better than junior physicians（DSC：90.15% vs. 68.73%， Z=-127.76， P<0.001），and approached the level of senior physician（DSC：90.15% vs. 86.15%， Z=-31.33， P=0.549）. The model demonstrated superior boundary recognition in complex anatomical structures（e.g.，C6/C7 nerve roots）compared to ultrasound physicians（junior and senior）（HD95：8.08 vs. 26.34，17.44，56.80）. Conclusions:This study proposes an analysis model for BP ultrasound images，CHA-TransUNet. This model achieves segmentation and recognition of the BP with relatively complex pathways and structures. The model exhibits high accuracy and stability，outperforming current mainstream network models and junior physicians while approaching the performance level of senior physicians. It assists junior physicians or trainees in more accurately identifying and localizing the BP.

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

(+)-Borneol, the main component of "Natural Borneol" in the Chinese Pharmacopoeia, is a high-end spice and precious medicine. Plant extraction cannot meet the increasing demand for (+)-borneol, while microbial biosynthesis offers a sustainable supply route. However, its production was extremely low compared with other monoterpenes, even with extensively optimizing the mevalonate pathway. We found that the key challenge is the complex and unusual dephosphorylation reaction of bornyl diphosphate (BPP), which suffers the side-reaction and the competition from the cellular dephosphorylation process, especially lipid metabolism, thus limiting (+)-borneol synthesis. Here, we systematically optimized the dephosphorylation process by identifying, characterizing phosphatases, and balancing cellular dephosphorylation metabolism. For the first time, we identified two endogenous phosphatases and seven heterologous phosphatases, which significantly increased (+)-borneol production by up to 152%. By engineering BPP dephosphorylation and optimizing the MVA pathway, the production of (+)-borneol was increased by 33.8-fold, which enabled the production of 753 mg/L under fed-batch fermentation in shake flasks, so far the highest reported in the literature. This study showed that rewiring dephosphorylation metabolism was essential for high-level production of (+)-borneol in Saccharomyces cerevisiae, and balancing cellular dephosphorylation is also helpful for efficient biosynthesis of other terpenoids since all whose biosynthesis involves the dephosphorylation procedure.

Objective:To explore the clinical application value of artificial intelligence models in identifying bleeding events and hemorrhagic points during laparoscopic pancreatic surgery.Methods:This single-center retrospective cohort study collected surgical videos of 25 patients undergoing laparoscopic pancreatic surgery at the Department of General Surgery, Peking Union Medical College Hospital from January 2022 to December 2024. Videos within 5 seconds before and after representative bleeding events were captured at 30 frames/s, with 11 666 hemorrhagic-related video frames annotated. Two algorithm models were developed: a pigment-based model and a pigment+optical flow-based model for classification and target recognition of bleeding frames. The training and test sets for the pigment-based algorithm contained 4 692 hemorrhagic and 4 309 non-hemorrhagic frames, while those for the pigment+optical flow model included 1 339 hemorrhagic and 1 326 non-hemorrhagic frames. Performance evaluation was conducted using overlap thresholds, with accuracy and recall rates as key metrics.Results:The pigment-based model achieved 93.8% accuracy (134/143) and 43.3% recall (134/310) in hemorrhagic frame classification. At an overlap threshold of 0.3, the pigment-based model showed 84.1% accuracy (433/515) and 85.4% recall (433/507) in target recognition. When the threshold was increased to 0.5, the pigment+optical flow model demonstrated 88.1% accuracy (354/402) and 89.2% recall (354/397) in hemorrhagic target recognition.Conclusions:It is difficult to distinguish active bleeding from old bleeding completely by pigment information alone. The spatio-temporal features can be effectively extracted by combining pigment and optical flow information, and the bleeding can be accurately identified and located, which has potential clinical application value.

Objective:To investigate the prevalence of skeletal fluorosis and the management and treatment of patients in endemic fluorosis areas of Shaanxi Province.Methods:From March to November 2023, in accordance with the requirements of the "2023 Monitoring Plan for Endemic Fluorosis in Shaanxi Province", the implementation of prevention and control measures of endemic fluorosis in Shaanxi Province was investigated. The data of patients with skeletal fluorosis from January to December 2023 were downloaded from Shaanxi Province Endemic Disease Control Information Management Platform, and the epidemiological characteristics were analyzed. At the same time, the management and treatment of patients with skeletal fluorosis in endemic fluorosis areas of Shaanxi Province were carried out in accordance with the requirements of the "Shaanxi Province Endemic Disease Patient Management Service Specification".Results:In 2023, there were 10 drinking water-borne endemic fluorosis cities in Shaanxi Province, involving 3 715 endemic villages, including 3 650 water improvement villages with a water-improving rate of 98.25%. There were 2 coal-burning-borne endemic fluorosis cities in Shaanxi Province, involving 641 746 households in 1 414 endemic villages. Among them, 641 617 households had changed their furnaces and stoves, with a furnace and stove change rate of 99.98%. There were 37 462 patients with skeletal fluorosis in the province, with 35 792 from drinking water-borne endemic fluorosis areas and 1 670 from coal-burning-borne endemic fluorosis areas. The condition was mainly mild and moderate (94.12%, 35 258/37 462), with females accounting for 55.13% (20 654/37 462). The education level was mainly primary school and illiteracy (83.64%, 31 333/37 462), and the occupation was mainly farmers (99.29%, 37 196/37 462). A total of 37 116 patients with skeletal fluorosis were followed up and managed, with a management rate of 99.08% (37 116/37 462). A total of 35 756 patients were managed in a standardized manner, and the standardized management rate was 96.34% (35 756/37 116). A total of 30 649 patients with skeletal fluorosis were actually treated, with a treatment rate of 84.27% (30 649/36 370) and a total effective rate of 98.08% (30 062/30 649).Conclusion:In 2023, the prevalence of skeletal fluorosis in endemic fluorosis areas of Shaanxi Province is mainly mild and moderate, with a wide coverage of community management and a high level of treatment efficiency.

Objective:To propose a deep learning（DL）-based ultrasound imaging auxiliary tool for automatic segmentation and recognition of the brachial plexus（BP），and to enhance the accuracy and safety of clinical procedures.Methods:It was a multicenter study that collected 773 healthy subjects from Peking University Third Hospital and its branch campuses，the Third Medical Center of the Chinese PLA General Hospital，and Shanghai Eighth People's Hospital between August 2024 and February 2025. Brachial plexus（BP）images in the interscalene groove were captured used high-frequency ultrasound by senior sonographers，a dataset comprising 1 289 standardized images were constructed and the improved model（CHA-TransUNet）was trained. The test set was input into 6 different models（CHA-TransUNet，R50-Unet，TransUnet，SegFormer，SwinUnet，MISSFormer）for segmentation. Segmentation accuracy was evaluated using metrics including the Dice similarity coefficient（DSC），95% Hausdorff distance（HD95）and mean intersection over union（mIoU），and was compared with the segmentation results of 3 ultrasound physicians with varying experience levels（junior physicians and senior physicians）to validate the model's segmentation efficacy.Results:The CHA-TransUNet model established based on a dataset of 1 289 standardized images achieved segmentation results for the BP with a DSC of 90.15%，mIoU of 91.02%，and HD95 of 8.08. Its accuracy was higher than other mainstream models（DSC：90.15% vs. 87.60%，87.77%，81.35%，84.78%，84.55%），significantly better than junior physicians（DSC：90.15% vs. 68.73%， Z=-127.76， P<0.001），and approached the level of senior physician（DSC：90.15% vs. 86.15%， Z=-31.33， P=0.549）. The model demonstrated superior boundary recognition in complex anatomical structures（e.g.，C6/C7 nerve roots）compared to ultrasound physicians（junior and senior）（HD95：8.08 vs. 26.34，17.44，56.80）. Conclusions:This study proposes an analysis model for BP ultrasound images，CHA-TransUNet. This model achieves segmentation and recognition of the BP with relatively complex pathways and structures. The model exhibits high accuracy and stability，outperforming current mainstream network models and junior physicians while approaching the performance level of senior physicians. It assists junior physicians or trainees in more accurately identifying and localizing the BP.

Objective:To explore the clinical application value of artificial intelligence models in identifying bleeding events and hemorrhagic points during laparoscopic pancreatic surgery.Methods:This single-center retrospective cohort study collected surgical videos of 25 patients undergoing laparoscopic pancreatic surgery at the Department of General Surgery, Peking Union Medical College Hospital from January 2022 to December 2024. Videos within 5 seconds before and after representative bleeding events were captured at 30 frames/s, with 11 666 hemorrhagic-related video frames annotated. Two algorithm models were developed: a pigment-based model and a pigment+optical flow-based model for classification and target recognition of bleeding frames. The training and test sets for the pigment-based algorithm contained 4 692 hemorrhagic and 4 309 non-hemorrhagic frames, while those for the pigment+optical flow model included 1 339 hemorrhagic and 1 326 non-hemorrhagic frames. Performance evaluation was conducted using overlap thresholds, with accuracy and recall rates as key metrics.Results:The pigment-based model achieved 93.8% accuracy (134/143) and 43.3% recall (134/310) in hemorrhagic frame classification. At an overlap threshold of 0.3, the pigment-based model showed 84.1% accuracy (433/515) and 85.4% recall (433/507) in target recognition. When the threshold was increased to 0.5, the pigment+optical flow model demonstrated 88.1% accuracy (354/402) and 89.2% recall (354/397) in hemorrhagic target recognition.Conclusions:It is difficult to distinguish active bleeding from old bleeding completely by pigment information alone. The spatio-temporal features can be effectively extracted by combining pigment and optical flow information, and the bleeding can be accurately identified and located, which has potential clinical application value.

The 2-(2-phenylethyl)chromones were separated from agarwood of Aquilaria agallocha Roxb. and their anti-KRAS mutant non-small cell lung cancer (NSCLC) activities were evaluated. 2-(2-Phenyethyl)chromones in agarwood were separated and purified by silica gel, RP-18 reverse phase silica gel, MCI gel CH20P, Diol, and semi preparative HPLC chromatography techniques, while the structures of the compounds were identified by extensive spectroscopic analysis, such as 1D and 2D NMR and ESI-MS. The cell counting kit 8 (CCK-8) assay was used to screen the anti-tumor activity of the isolated monomeric compounds on three KRAS-mutant NSCLC cells. The cell proliferation, cloning formation, adhesion ability, and cell cycle arrest activity of compound 8 were analyzed. Molecular docking and Western blot experiments were used to study the mechanism of compound 8. The in vivo anti-tumor activity of the compound 8 was evaluated by zebrafish cell derived xenograft (CDX) model. Nineteen known 2-(2-phenylethyl)chromones were isolated from the ethyl acetate extract of agarwood. On A549 (KRAS G12S) cells, compounds 8, 10, and 19 showed good inhibitory activity, on H23 (KRAS G12C) cells, compounds 7, 8, and 19 showed good inhibitory activity, and on H358 (KRAS G12C) cells, compounds 8, 10, and 16 showed good inhibitory activity. Compound 8 had the best inhibitory activity in all three cell lines. It effectively inhibited cell proliferation, clone formation, adhesion ability, and arrested the H23 cell cycle at G2/M phase. Compound 8 could also inhibit the expression of c-Met and its downstream signaling pathways, effectively inhibiting tumor growth in zebrafish CDX model. In conclusion, among the nineteen known 2-(2-phenylethyl)chromones, compound 8 had the best activity and significantly inhibited the proliferation of KRAS-mutant NSCLC cells by arresting the cells in the G2/M phase.