Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

INTRODUCTION: The primary objective of this guideline is to establish evidence-based recommendations for the clinical use of parenteral nutrition (PN) in adult patients within the acute hospital setting in Singapore. METHOD: An expert workgroup, consisting of healthcare practitioners actively involved in clinical nutrition support across all public health institutions, systematically evaluated existing evidence and addressed clinical questions relating to PN therapy. RESULTS: This clinical practice guideline developed 30 recommendations for PN therapy, which cover these key aspects related to PN use: indications, patient assess-ment, titration and formulation of PN bags, access routes and devices, and monitoring and management of PN-related complications. CONCLUSION This guideline provides recommendations to ensure appropriate and safe clinical practice of PN therapy in adult patients within the acute hospital setting.
Humans ; Singapore ; Parenteral Nutrition/adverse effects* ; Adult

INTRODUCTION: Paediatric sexual assault (SA) victims should be assessed for post-exposure prophylaxis (PEP) to mitigate the risk of sexually transmitted infections (STIs). We describe the clinical characteristics of children and young persons (CYPs) presenting with SA at KK Women's and Children's Hospital in Singapore, viral PEP (human immunodeficiency virus [HIV] and hepatitis B virus [HBV]) prescribing practices, and STI evaluation at follow-up. METHOD: Medical records of CYPs ≤16 years who presented with SA between January 2022 and August 2023 were reviewed, including assault and assailant characteristics, baseline and follow-up STI screening, PEP prescription, adherence and follow-up attendance. CYPs with SA in the preceding 72 hours by HIV-positive or HIV-status unknown assailants with high-risk characteris-tics were eligible for HIV PEP. RESULTS: We analysed 278 CYPs who made 292 SA visits. There were 40 (13.7%) CYPs eligible for HIV PEP, of whom 29 (82.9%) received it. Among those tested at baseline, 9% and 34.9% of CYPs tested positive for Chlamydia trachomatis and Gardnerella vaginalis, respectively. None tested positive for Neisseria gonorrhoeae, Trichomonas vaginalis, HIV, HBV or hepatitis C. Majority of CYPs tested were HBV non-immune (n=167, 67.6%); only 77 (46.1%) received the vaccine. Out of 27 CYPs eligible for HBV PEP with immunoglobulin, only 21 (77.7%) received immunoglobulin. A total of 37 CYPs received HIV PEP, including 8 who were retrospectively deemed ineligible. Only 10 (27%) completed the course. Overall, 153 (57.7%) CYPs attended follow-up, and none seroconverted for HIV or HBV. CONCLUSION We report suboptimal rates of HBV post-exposure vaccination, and low compliance to HIV PEP and follow-up among paediatric SA victims. Factors contri-buting to poor compliance should be examined to optimise care for this vulnerable population.
Humans ; Post-Exposure Prophylaxis/methods* ; Female ; Child ; Adolescent ; Singapore/epidemiology* ; HIV Infections/prevention & control* ; Male ; Sexually Transmitted Diseases/epidemiology* ; Retrospective Studies ; Hepatitis B/prevention & control* ; Follow-Up Studies ; Child, Preschool ; Sex Offenses/statistics & numerical data* ; Child Abuse, Sexual