With the rapid advancement of hemodynamic indices and monitoring technologies, their classification methods and application processes have become increasingly complex. Currently, no unified standard hasbeen established, making it difficult to fully meet the clinical requirements for hemodynamic management. To assist in hemodynamic monitoring assessment and therapeutic decision-making in critically ill patients, the Critical Hemodynamic Therapy Collaborative Group, in conjunction with the Critical Ultrasound Study Group, has jointly developed the Standard for the Application of Hemodynamic Monitoring Techniques in Critical Care. The first part of this standard systematically categorizes hemodynamic indicators into flow indicators, pressure and its derivative indicators, and tissue perfusion indicators, while elaborating on the clinical application of each. The second part establishes a standardized clinical implementation pathway for hemodynamic monitoring. It proposes a tiered monitoring strategy-comprising basic, advanced, indication-specific, and special scenario monitoring-tailored to different clinical settings. It emphasizes the central role of critical care ultrasound across all levels of monitoring and establishes hemodynamic assessment standards for organs such as the brain, kidneys, and gastrointestinal tract. This standard aims to provide a unified framework for clinical practice, teaching, training, and research in critical care medicine, thereby promoting standardized development within the discipline.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

OBJECTIVES: To study genotype-phenotype correlations in children with FGFR3 variants and to improve clinical recognition of related disorders. METHODS: Clinical data of 95 patients aged 0-18 years harboring FGFR3 variants, confirmed by whole‑exome sequencing at Shanghai Children's Medical Center from January 2012 to December 2023, were retrospectively reviewed. Detailed phenotypic characterization was performed for 22 patients with achondroplasia (ACH) and 10 with hypochondroplasia (HCH). RESULTS: Among the 95 patients, 52 (55%) had ACH, 24 (25%) had HCH, 9 (9%) had thanatophoric dysplasia, 3 (3%) had syndromic skeletal dysplasia, 2 (2%) had severe achondroplasia with developmental delay and acanthosis nigricans, and 5 (5%) remained unclassified. A previously unreported FGFR3 variant, c.1663G>T, was identified. All 22 ACH patients presented with disproportionate short stature accompanied by limb dysplasia, commonly with macrocephaly, a depressed nasal bridge, bowed legs, and frontal bossing; complications were present in 17 (77%). The 10 HCH patients predominantly exhibited disproportionate short stature with limb dysplasia and depressed nasal bridge. CONCLUSIONS ACH is the most frequent phenotype associated with FGFR3 variants, and missense variants constitute the predominant variant type. The degree of FGFR3 activation appears to correlate with the clinical severity of skeletal dysplasia.
Humans ; Receptor, Fibroblast Growth Factor, Type 3/genetics* ; Child ; Male ; Child, Preschool ; Female ; Infant ; Adolescent ; Dwarfism/genetics* ; Achondroplasia/genetics* ; Lordosis/genetics* ; Infant, Newborn ; Retrospective Studies ; Genetic Association Studies ; Bone and Bones/abnormalities* ; Phenotype ; Limb Deformities, Congenital

OBJECTIVE: To explore the efficacy and apoptosis of allogeneic hematopoietic stem cell transplantation (allo-HSCT) in the treatment of acute myeloid leukemia (AML) with ASXL1 mutation. METHODS: The clinical data of 80 AML patients with ASXL1 mutation treated in our hospital from January 2019 to December 2021 were retrospectively analyzed. The clinical characteristics of the patients were summarized, and the therapeutic effect and prognostic factors of allo-HSCT for the patients were analyzed. RESULTS: Among the 80 patients, 38 were males and 42 were females, and the median age was 39(14-65) years. There were 17 patients in low-risk group, 25 patients in medium-risk group and 38 patients in high-risk group. ASXL1 mutation co-occurred with many other gene mutations, and the frequent mutated genes were TET2 (71.25%), NRAS (18.75%), DNMT3A (16.25%), NPM1 (15.00%), CEBPA (13.75%). Among medium and high-risk patients, 29 underwent allo-HSCT, while 34 received chemotherapy. The 2-year overall survival (OS) rate and disease-free survival (DFS) rate of the allo-HSCT group were 72.4% and 70.2%, while those of the chemotherapy group were 44.1% and 34.0%, respectively. The statistical analysis showed significant differences between the two groups (both P < 0.01). Multivariate analysis showed that age at transplantation >50- years and occurrence of acute graft-versus-host disease after transplantation were poor prognostic factors for OS and DFS in transplantation patients. CONCLUSION Allo-HSCT can improve the prognosis of AML patients with ASXL1 mutation.
Humans ; Leukemia, Myeloid, Acute/therapy* ; Hematopoietic Stem Cell Transplantation ; Female ; Male ; Middle Aged ; Mutation ; Adult ; Repressor Proteins/genetics* ; Adolescent ; Retrospective Studies ; Aged ; Nucleophosmin ; Young Adult ; Transplantation, Homologous ; Prognosis ; Survival Rate

OBJECTIVE: To analyze the laboratory detection results of hemolytic disease of the fetus and newborn(HDFN). METHODS: Related test results of 283 newborns and their mothers' blood samples from Kunming Maternal and Child Health Hospital from August 2023 to May 2024 were collected, including mother and child ABO blood group, RhD blood group, as well as 3 tests of HDFN, total bilirubin (TBil) and indirect bilirubin (IBil). RESULTS: 283 were ABO incompatibility, among which 187 were HDFN positive, with a positive rate of 66.08%; the positive rate of HDFN in neonates with antigen-A incompatibility was 74.12%(126/170), the positive rate of HDFN in neonates with antigen-B incompatibility was 53.57%(60/112), which was the highest in neonates with O/A incompatibility ［75.45%(126/167)］, followed by O/B incompatibility［54.55%(60/110)］. Group by age, the positive rates of HDFN in the ≤1 d group, 2 d group, 3 d group, 4 d group, 5 d group and ≥6 d group were 76.03%(111/146), 67.86%(38/56), 57.14%(24/42), 38.46%(5/13), 46.15%(6/13) and 23.08%(3/13), respectively. With the increase of age, the positive rates of HDFN gradually decreased, there was a statistically significant difference between the ≤3 day age group and >3 day age group ( P <0.05). There was no statistically significant difference in TBil and IBil levels between the "direct antibody+indirect antibody+release+" group and the HDFN negative group in newborns. HDFN infants exhibited a rapid increase in bilirubin levels within the first day after birth, with significantly higher TBil and IBil values compared to Non ABO-HDFN infants in the ≤1 day group ( P <0.01). However, the difference of bilirubin levels between the two groups gradually narrowed from 2-6 days after birth, and the difference was not statistically significant (P >0.05). The peak value of TBil and IBil occurred on the 4th day after birth in HDFN infants. CONCLUSION ABO-HDFN is most commonly seen in newborns whose mothers are type-O, and the positive rate was the highest in newborns with O/A incompatibility. The detection rate of HDFN is affected by the age of the newborns, and the two were correlated inversely. ABO-HDFN group developed more rapidly with a higher peak. Therefore, HDFN tests should be carried out as soon as possible for mothers and newborns with incompatible blood types, and appropriate treatment should be provided to prevent complications.
Humans ; Infant, Newborn ; ABO Blood-Group System ; Erythroblastosis, Fetal/epidemiology* ; Female ; China/epidemiology* ; Blood Group Incompatibility ; Male ; Bilirubin/blood*

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.