Numerous c-mesenchymal-epithelial transition (c-MET) inhibitors have been reported as potential anticancer agents. However, most fail to enter clinical trials owing to poor efficacy or drug resistance. To date, the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed. In this study, we constructed the largest c-MET dataset, which included 2,278 molecules with different structures, by inhibiting the half maximal inhibitory concentration (IC₅₀) of kinase activity. No significant differences in drug-like properties were observed between active molecules (1,228) and inactive molecules (1,050), including chemical space coverage, physicochemical properties, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles. The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding (t-SNE) high-dimensional data. Further clustering and chemical space networks (CSNs) analyses revealed commonly used scaffolds for c-MET inhibitors, such as M5, M7, and M8. Activity cliffs and structural alerts were used to reveal "dead ends" and "safe bets" for c-MET, as well as dominant structural fragments consisting of pyridazinones, triazoles, and pyrazines. Finally, the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules, including at least three aromatic heterocycles, five aromatic nitrogen atoms, and eight nitrogen-oxygen atoms. Overall, our analyses revealed potential structure-activity relationship (SAR) patterns for c-MET inhibitors, which can inform the screening of new compounds and guide future optimization efforts.

BACKGROUND: Delayed platelet engraftment is a common complication after umbilical cord blood transplantation (UCBT), and there is no standard therapy. Avatrombopag (AVA) is a second-generation thrombopoietin (TPO) receptor agonist (TPO-RA) that has shown efficacy in immune thrombocytopenia (ITP). However, few reports have focused on its efficacy in patients diagnosed with thrombocytopenia after allogeneic hematopoietic stem cell transplantation (allo-HSCT). METHODS: We conducted a retrospective study at the First Affiliated Hospital of the University of Science and Technology of China to evaluate the efficacy of AVA as a first-line TPO-RA in 65 patients after UCBT; these patients were compared with 118 historical controls. Response rates, platelet counts, megakaryocyte counts in bone marrow, bleeding events, adverse events and survival rates were evaluated in this study. Platelet reconstitution differences were compared between different medication groups. Multivariable analysis was used to explore the independent beneficial factors for platelet implantation. RESULTS: Fifty-two patients were given AVA within 30 days post-UCBT, and the treatment was continued for more than 7 days to promote platelet engraftment (AVA group); the other 13 patients were given AVA for secondary failure of platelet recovery (SFPR group). The median time to platelet engraftment was shorter in the AVA group than in the historical control group (32.5 days vs . 38.0 days, Z  = 2.095, P  = 0.036). Among the 52 patients in the AVA group, 46 achieved an overall response (OR) (88.5%), and the cumulative incidence of OR was 91.9%. Patients treated with AVA only had a greater 60-day cumulative incidence of platelet engraftment than patients treated with recombinant human thrombopoietin (rhTPO) only or rhTPO combined with AVA (95.2% vs . 84.5% vs . 80.6%, P  <0.001). Patients suffering from SFPR had a slightly better cumulative incidence of OR (100%, P  = 0.104). Patients who initiated AVA treatment within 14 days post-UCBT had a better 60-day cumulative incidence of platelet engraftment than did those who received AVA after 14 days post-UCBT (96.6% vs . 73.9%, P  = 0.003). CONCLUSION Compared with those in the historical control group, our results indicate that AVA could effectively promote platelet engraftment and recovery after UCBT, especially when used in the early period (≤14 days post-UCBT).
Humans ; Female ; Male ; Thrombocytopenia/etiology* ; Adult ; Retrospective Studies ; Cord Blood Stem Cell Transplantation/adverse effects* ; Middle Aged ; Adolescent ; Young Adult ; Thiazoles/adverse effects* ; Platelet Count ; Receptors, Thrombopoietin/agonists* ; Child ; Thiophenes

Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed.In this study,we constructed the largest c-MET dataset,which included 2,278 molecules with different struc-tures,by inhibiting the half maximal inhibitory concentration(IC50)of kinase activity.No significant differences in drug-like properties were observed between active molecules(1,228)and inactive mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,metabolism,excretion,and toxicity(ADMET)profiles.The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding(t-SNE)high-dimensional data.Further clustering and chemical space networks(CSNs)analyses revealed commonly used scaffolds for c-MET inhibitors,such as M5,M7,and M8.Activity cliffs and structural alerts were used to reveal"dead ends"and"safe bets"for c-MET,as well as dominant structural fragments consisting of pyr-idazinones,triazoles,and pyrazines.Finally,the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules,including at least three aromatic het-erocycles,five aromatic nitrogen atoms,and eight nitrogen-oxygen atoms.Overall,our analyses revealed potential structure-activity relationship(SAR)patterns for c-MET inhibitors,which can inform the screening of new compounds and guide future optimization efforts.

Objectives: This study aimed to present the Asia-Pacific consensus on long-term and sequential therapy for osteoporosis, offering evidence-based recommendations for the effective management of this chronic condition.The primary focus is on achieving optimal fracture prevention through a comprehensive, individualized approach. Methods: A panel of experts convened to develop consensus statements by synthesizing the current literature and leveraging clinical expertise. The review encompassed long-term anti-osteoporosis medication goals, first-line treatments for individuals at very high fracture risk, and the strategic integration of anabolic and anti resorptive agents in sequential therapy approaches. Results: The panelists reached a consensus on 12 statements. Key recommendations included advocating for anabolic agents as the first-line treatment for individuals at very high fracture risk and transitioning to anti resorptive agents following the completion of anabolic therapy. Anabolic therapy remains an option for in dividuals experiencing new fractures or persistent high fracture risk despite antiresorptive treatment. In cases of inadequate response, the consensus recommended considering a switch to more potent medications. The consensus also addressed the management of medication-related complications, proposing alternatives instead of discontinuation of treatment. Conclusions This consensus provides a comprehensive, cost-effective strategy for fracture prevention with an emphasis on shared decision-making and the incorporation of country-specific case management systems, such as fracture liaison services. It serves as a valuable guide for healthcare professionals in the Asia-Pacific region, contributing to the ongoing evolution of osteoporosis management.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective:To investigate the effect of laboratory indicators on hair loss in patients with female pattern hair loss (FPHL).Methods:Patients with FPHL who visited the Outpatient Clinic of the Department of Medical Aesthetics in Hangzhou First People’s Hospital from November 2022 to November 2023 were selected as the study group, and healthy women who matched the age of the study group in the physical examination center during the same period were selected as the control group. The general information of the patient was recorded, and was also tested by trichoscopy to rule out other patterns of alopecia. Representative indicators including testosterone, dehydroepiandrosterone sulfate(DHEA-S), thyroid-stimulating hormone, 25-hydroxyvitamin D, and serum ferritin were selected from laboratory tests for further analysis. Otherwise, the proportion of deficiency in vitamin D(<20 ng/ml) was calculated based on 25-hydroxyvitamin D levels (number of deficiency cases/total number of cases in each group×100%). Count data were presented as samples (percentages), and chi-square test was used for comparison between groups. Normally distributed continuous data were presented with Mean±SD, independent samples t-test was used for comparison between groups, M( Q1, Q3) was used for non-normally distributed continuous data, and Wilcoxon rank-sum test was used for comparison between groups. Multivariate logistic regression was used to analyze the influencing factors of FPHL. P<0.05 was statistically significant. Results:A total of 37 patients were selected in both groups. The mean age was (28.8±1.3) years in the study group and (29.6±0.9) years in the control group ( t=0.49, P=0.625). The body mass index was (22.8±0.4) kg/m 2 in the study group, and (23.5±0.3) kg/m 2 in the control group ( t=1.26, P=0.211). The testosterone level was 0.58 (0.49, 0.79) nmol/L in the study group, and 0.54 (0.50, 0.78) nmol/L in the control group( Z=1.42, P=0.157). The level of DHEA-S was 6.21 (5.18, 9.60) μmol/L in the study group, and 6.20 (5.20, 9.34) μmol/L in the control group ( Z=2.75, P=0.006). The level of thyroid-stimulating hormone was 2.56 (1.55, 3.66) mU/L in the study group and 1.49 (1.05, 2.65) mU/L in the control group ( Z=2.51, P=0.012). The level of 25-hydroxyvitamin D was 15.44 (11.80, 21.20) ng/ml in the study group, and the level of 25-hydroxyvitamin D was 20.32 (12.07, 21.20) ng/ml in the control group ( Z=2.30, P=0.021), and the proportion of 25-hydroxyvitamin D deficiency in the study group was 64.9% (24/37), which was higher than that in the control group [40.5% (15/37)] ( χ2=4.39, P=0.036). The serum ferritin level was 64.44 (39.47, 133.45) μg/L in the study group and 67.75 (52.63, 143.83) μg/L in the control group ( Z=0.70, P=0.484). The results of multivariate logistic regression analysis showed that the risk of FPHL was increased by the high level of DHEA-S and thyroid-stimulating hormone, and the low level of 25-hydroxyvitamin D (all P<0.05). Conclusion:Abnormal level of DHEA-S, thyroid-stimulating hormone, and 25-hydroxyvitamin D may be risk factors for FPHL.

Objective:To investigate the effect of laboratory indicators on hair loss in patients with female pattern hair loss (FPHL).Methods:Patients with FPHL who visited the Outpatient Clinic of the Department of Medical Aesthetics in Hangzhou First People’s Hospital from November 2022 to November 2023 were selected as the study group, and healthy women who matched the age of the study group in the physical examination center during the same period were selected as the control group. The general information of the patient was recorded, and was also tested by trichoscopy to rule out other patterns of alopecia. Representative indicators including testosterone, dehydroepiandrosterone sulfate(DHEA-S), thyroid-stimulating hormone, 25-hydroxyvitamin D, and serum ferritin were selected from laboratory tests for further analysis. Otherwise, the proportion of deficiency in vitamin D(<20 ng/ml) was calculated based on 25-hydroxyvitamin D levels (number of deficiency cases/total number of cases in each group×100%). Count data were presented as samples (percentages), and chi-square test was used for comparison between groups. Normally distributed continuous data were presented with Mean±SD, independent samples t-test was used for comparison between groups, M( Q1, Q3) was used for non-normally distributed continuous data, and Wilcoxon rank-sum test was used for comparison between groups. Multivariate logistic regression was used to analyze the influencing factors of FPHL. P<0.05 was statistically significant. Results:A total of 37 patients were selected in both groups. The mean age was (28.8±1.3) years in the study group and (29.6±0.9) years in the control group ( t=0.49, P=0.625). The body mass index was (22.8±0.4) kg/m 2 in the study group, and (23.5±0.3) kg/m 2 in the control group ( t=1.26, P=0.211). The testosterone level was 0.58 (0.49, 0.79) nmol/L in the study group, and 0.54 (0.50, 0.78) nmol/L in the control group( Z=1.42, P=0.157). The level of DHEA-S was 6.21 (5.18, 9.60) μmol/L in the study group, and 6.20 (5.20, 9.34) μmol/L in the control group ( Z=2.75, P=0.006). The level of thyroid-stimulating hormone was 2.56 (1.55, 3.66) mU/L in the study group and 1.49 (1.05, 2.65) mU/L in the control group ( Z=2.51, P=0.012). The level of 25-hydroxyvitamin D was 15.44 (11.80, 21.20) ng/ml in the study group, and the level of 25-hydroxyvitamin D was 20.32 (12.07, 21.20) ng/ml in the control group ( Z=2.30, P=0.021), and the proportion of 25-hydroxyvitamin D deficiency in the study group was 64.9% (24/37), which was higher than that in the control group [40.5% (15/37)] ( χ2=4.39, P=0.036). The serum ferritin level was 64.44 (39.47, 133.45) μg/L in the study group and 67.75 (52.63, 143.83) μg/L in the control group ( Z=0.70, P=0.484). The results of multivariate logistic regression analysis showed that the risk of FPHL was increased by the high level of DHEA-S and thyroid-stimulating hormone, and the low level of 25-hydroxyvitamin D (all P<0.05). Conclusion:Abnormal level of DHEA-S, thyroid-stimulating hormone, and 25-hydroxyvitamin D may be risk factors for FPHL.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.