Kirsten rat sarcoma viral oncogene homolog(KRAS)protein inhibitors are a promising class of thera-peutics,but research on molecules that effectively penetrate the blood-brain barrier(BBB)remains limited,which is crucial for treating central nervous system(CNS)malignancies.Although molecular generation models have recently advanced drug discovery,they often overlook the complexity of bio-logical and chemical factors,leaving room for improvement.In this study,we present a structure-constrained molecular generation workflow designed to optimize lead compounds for both drug effi-cacy and drug absorption properties.Our approach utilizes a variational autoencoder(VAE)generative model integrated with reinforcement learning for multi-objective optimization.This method specifically aims to enhance BBB permeability(BBBp)while maintaining high-affinity substructures of KRAS in-hibitors.To support this,we incorporate a specialized KRAS BBB predictor based on active learning and an affinity predictor employing comparative learning models.Additionally,we introduce two novel metrics,the knowledge-integrated reproduction score(KIRS)and the composite diversity score(CDS),to assess structural performance and biological relevance.Retrospective validation with KRAS inhibitors,AMG510 and MRTX849,demonstrates the framework's effectiveness in optimizing BBBp and highlights its potential for real-world drug development applications.This study provides a robust framework for accelerating the structural enhancement of lead compounds,advancing the drug development process across diverse targets.

Kirsten rat sarcoma viral oncogene homolog (KRAS) protein inhibitors are a promising class of therapeutics, but research on molecules that effectively penetrate the blood-brain barrier (BBB) remains limited, which is crucial for treating central nervous system (CNS) malignancies. Although molecular generation models have recently advanced drug discovery, they often overlook the complexity of biological and chemical factors, leaving room for improvement. In this study, we present a structure-constrained molecular generation workflow designed to optimize lead compounds for both drug efficacy and drug absorption properties. Our approach utilizes a variational autoencoder (VAE) generative model integrated with reinforcement learning for multi-objective optimization. This method specifically aims to enhance BBB permeability (BBBp) while maintaining high-affinity substructures of KRAS inhibitors. To support this, we incorporate a specialized KRAS BBB predictor based on active learning and an affinity predictor employing comparative learning models. Additionally, we introduce two novel metrics, the knowledge-integrated reproduction score (KIRS) and the composite diversity score (CDS), to assess structural performance and biological relevance. Retrospective validation with KRAS inhibitors, AMG510 and MRTX849, demonstrates the framework's effectiveness in optimizing BBBp and highlights its potential for real-world drug development applications. This study provides a robust framework for accelerating the structural enhancement of lead compounds, advancing the drug development process across diverse targets.

Objective:To systematically evaluate studies validating the performance of the Global Leadership Initiative on Malnutrition (GLIM) in diagnosing malnutrition.Methods:Seven Chinese and English databases including Embase, Web of Science (WOS), PubMed, CINAHL, Cochrane Library, SinoMed, CNKI, Wanfang Data, and VIP Database were searched for articles on the validation of GLIM criteria published between September 2018 and September 2024. Two researchers independently performed literature screening and data extraction. The concurrent and predictive validity of the criteria was analyzed.Results:A total of 136 papers were included for analysis. The GLIM criteria for diagnosing malnutrition had a sensitivity of 77%, a specificity of 87%, and an area under the curve (AUC) of 0.90. Malnutrition diagnosed by the GLIM criteria predicted prolonged hospital and intensive care unit (ICU) stays, increased readmission and complication rates (both overall and infectious), reduced survivals (median, overall, and disease-free), and increased in-hospital and follow-up mortalities. Both moderate and severe malnutrition predicted decreased overall survival. However, only three studies analyzed the impact of nutritional therapy on the clinical outcomes of malnourished patients.Conclusions:The GLIM criteria accurately differentiate malnutrition and are a valid predictive tool of clinical outcomes. However, the validity criteria in these validation studies were questionable, along with high methodological heterogeneity. Furthermore, there is a lack of studies validating the role of nutritional therapy in improving the clinical outcomes of malnourished patients.

Objective:To systematically evaluate studies validating the performance of the Global Leadership Initiative on Malnutrition (GLIM) in diagnosing malnutrition.Methods:Seven Chinese and English databases including Embase, Web of Science (WOS), PubMed, CINAHL, Cochrane Library, SinoMed, CNKI, Wanfang Data, and VIP Database were searched for articles on the validation of GLIM criteria published between September 2018 and September 2024. Two researchers independently performed literature screening and data extraction. The concurrent and predictive validity of the criteria was analyzed.Results:A total of 136 papers were included for analysis. The GLIM criteria for diagnosing malnutrition had a sensitivity of 77%, a specificity of 87%, and an area under the curve (AUC) of 0.90. Malnutrition diagnosed by the GLIM criteria predicted prolonged hospital and intensive care unit (ICU) stays, increased readmission and complication rates (both overall and infectious), reduced survivals (median, overall, and disease-free), and increased in-hospital and follow-up mortalities. Both moderate and severe malnutrition predicted decreased overall survival. However, only three studies analyzed the impact of nutritional therapy on the clinical outcomes of malnourished patients.Conclusions:The GLIM criteria accurately differentiate malnutrition and are a valid predictive tool of clinical outcomes. However, the validity criteria in these validation studies were questionable, along with high methodological heterogeneity. Furthermore, there is a lack of studies validating the role of nutritional therapy in improving the clinical outcomes of malnourished patients.

Objective To explore the relationship between hemoglobin (Hb) level during the first trimester of pregnancy and gestational diabetes mellitus (GDM). Methods A total of 1 276 participants, who underwent scheduled prenatal examination and normal singleton delivery at the Fifth People′s Hospital of Shanghai and Hospital of Intergrated Chinese and Western Medicine in Minhang District, from January 2016 to May 2018 were included. There were 99 cases of GDM (GDM group) and 1 177 cases of normal (control group) pregnant women.Based on the serum Hb level during the first trimester of pregnancy, participants were divided into three groups, 236 cases of low Hb level group (Hb<110 g/L), 868 cases of normal Hb level group (110 g/L≤Hb<130 g/L), and 172 cases of high Hb level group (Hb≥130 g/L). Maternal clinical data were collected, including Hb level during the first trimester of pregnancy, three-point blood glucose (BG) of oral glucose tolerance test (OGTT) and fasting insulin during the second trimester of pregnancy. Homeostasis model assessment of insulin resistance index (HOMA-IR) and homeostasis model assessment of pancreatic β cell function index (HOMA-β) were used to evaluate insulin resistance and pancreatic β cell function. Results (1) Hb level during the first trimester of pregnancy in GDM group was significantly higher than that in control group [(123±10),(119±11) g/L, P<0.05]. There were no significant difference in gravidity, parity, index of liver and renal function (all P>0.05). (2) Pre-pregnancy body mass index (BMI), 1-hour BG and 2-hour BG of OGTT were significantly increased in the high Hb level group during the first trimester of pregnancy, which were (23±4) kg/m2, (7.3±2.0) mmol/L, and (6.5±1.4) mmol/L (P<0.05), respectively. The pre-pregnancy BMI, 1-hour BG and 2-hour BG of the normal or low Hb level group were (22±3) kg/m2, (6.7±1.6) mmol/L, (6.1± 1.2) mmol/L; (22±3) kg/m2, (6.5±1.5) mmol/L, (5.9±1.1) mmol/L, respectively. There were no statistically significant difference in levels of fasting blood glucose, fasting insulin, HOMA-IR and HOMA-β within 3 groups (all P>0.05). (3) In the high Hb level group, prevalence of pregnancy overweight or obesity and GDM were the highest, which were 37.2%(64/172) and 15.1%(26/172), respectively; the differences were statistically significant (all P<0.05). (4) The serum Hb level in the first trimester was positively related with pre-pregnancy BMI (r=0.130, P<0.05), 1-hour BG (r=0.129, P<0.05), 2-hour BG (r=0.134, P<0.05), fasting insulin (r=0.096, P<0.05), and HOMA-IR (r=0.101, P<0.05).Logistic regression indicated that Hb≥130 g/L during the first trimester of pregnancy was an independent risk factor for GDM ( OR=2.799, 95% CI :1.186-6.604; P<0.05). Conclusion The high level of Hb (Hb≥130 g/L) during the first trimester of pregnancy is associated with GDM.

Objective: To explore the relationship between hemoglobin (Hb) level during the first trimester of pregnancy and gestational diabetes mellitus (GDM). Methods: A total of 1 276 participants, who underwent scheduled prenatal examination and normal singleton delivery at the Fifth People's Hospital of Shanghai and Hospital of Intergrated Chinese and Western Medicine in Minhang District, from January 2016 to May 2018 were included. There were 99 cases of GDM (GDM group) and 1 177 cases of normal (control group) pregnant women.Based on the serum Hb level during the first trimester of pregnancy, participants were divided into three groups, 236 cases of low Hb level group (Hb<110 g/L), 868 cases of normal Hb level group (110 g/L≤Hb<130 g/L), and 172 cases of high Hb level group (Hb≥130 g/L). Maternal clinical data were collected, including Hb level during the first trimester of pregnancy, three-point blood glucose (BG) of oral glucose tolerance test (OGTT) and fasting insulin during the second trimester of pregnancy. Homeostasis model assessment of insulin resistance index (HOMA-IR) and homeostasis model assessment of pancreatic β cell function index (HOMA-β) were used to evaluate insulin resistance and pancreatic β cell function. Results: (1) Hb level during the first trimester of pregnancy in GDM group was significantly higher than that in control group [(123±10),(119±11) g/L, P<0.05]. There were no significant difference in gravidity, parity, index of liver and renal function (all P>0.05). (2) Pre-pregnancy body mass index (BMI), 1-hour BG and 2-hour BG of OGTT were significantly increased in the high Hb level group during the first trimester of pregnancy, which were (23±4) kg/m², (7.3±2.0) mmol/L, and (6.5±1.4) mmol/L (P<0.05), respectively. The pre-pregnancy BMI, 1-hour BG and 2-hour BG of the normal or low Hb level group were (22±3) kg/m², (6.7±1.6) mmol/L, (6.1±1.2) mmol/L; (22±3) kg/m², (6.5±1.5) mmol/L, (5.9±1.1) mmol/L, respectively. There were no statistically significant difference in levels of fasting blood glucose, fasting insulin, HOMA-IR and HOMA-β within 3 groups (all P>0.05). (3) In the high Hb level group, prevalence of pregnancy overweight or obesity and GDM were the highest, which were 37.2%(64/172) and 15.1%(26/172), respectively; the differences were statistically significant (all P<0.05). (4) The serum Hb level in the first trimester was positively related with pre-pregnancy BMI (r=0.130, P<0.05), 1-hour BG (r=0.129, P<0.05), 2-hour BG (r=0.134, P<0.05), fasting insulin (r=0.096, P<0.05), and HOMA-IR (r=0.101, P<0.05).Logistic regression indicated that Hb≥130 g/L during the first trimester of pregnancy was an independent risk factor for GDM (OR=2.799, 95%CI: 1.186-6.604; P<0.05). Conclusion The high level of Hb (Hb≥130 g/L) during the first trimester of pregnancy is associated with GDM.