Treatment Outcome Variables Research Articles

Process Control of Multistep Surface Functionalization on Hydroxyethyl Starch Nanocapsules Determines the Reproducibility of the Biological Efficacy.

Nanocarrier synthesis is highly process-dependent, leading to potential batch-to-batch variability if it is not controlled at each step. This variability affects the reproducibility of subsequent biomodification, resulting in unpredictable biological effects, particularly for bioactive molecules such as interleukin-2 (IL-2). Inconsistent conjugation can lead to variable treatment outcomes and severe side effects. Therefore, precise control of each synthesis step is critical for ensuring a consistent quality and biological performance. Our study demonstrates that dividing nanocarrier synthesis into smaller, controlled steps improves reproducibility. Using this method, we achieved highly reproducible, concentration-dependent growth of CTLL-2 cells with hydroxyethyl starch (HES) nanocapsules functionalized with defined amounts of IL-2. We believe that such detailed, stepwise control in nanocarrier synthesis enhances batch consistency, improving the clinical applicability of the drug delivery systems.

Isoform-level expression of the constitutive androstane receptor (CAR or NR1I3) transcription factor better predicts the mRNA expression of the cytochrome P450s in human liver samples.

Many factors cause inter-person variability in the activity and expression of liver cytochrome P450 (CYP) drug-metabolizing enzymes, leading to variable drug exposure and treatment outcomes. Several liver-enriched transcription factors (TFs) are associated with CYP expression, with estrogen receptor alpha (ESR1) and constitutive androstane receptor (CAR or NR1I3) being the two top factors. ESR1 and NR1I3 undergo extensive alternative splicing that results in numerous splice isoforms, but how these splice isoforms associate with CYP expression is unknown. Here, we quantified 18 NR1I3 splice isoforms and the three most abundant ESR1 isoforms in 260 liver samples derived from African Americans (AA, n=125) and European Americans (EA, n=135). Our results showed variable splice isoform populations in the liver for both NR1I3 and ESR1. Multiple linear regression analyses revealed that, compared to gene-level NR1I3, isoform-level NR1I3 expression better predicted the mRNA expression of most CYPs and three UDP-glucuronosyltransferases (UGTs), while ESR1 isoforms improved predictive models for the UGTs and CYP2D6, but not for most CYPs. Also, different NR1I3 isoforms were associated with different CYPs, and the associations varied depending on sample ancestry. Surprisingly, non-canonical NR1I3 isoforms having retained introns (introns 2 or 6) were abundantly expressed and associated with the expression of most CYPs and UGTs, whereas the reference isoform (NR1I3-205) only associated with CYP2D6. Moreover, NR1I3 isoform diversity increased during the differentiation of induced pluripotent stem cells to hepatocytes, paralleling increasing CYP expression. These results suggest that isoform-level TF expression may help to explain variation in CYP or UGT expression between individuals. Significance Statement We quantified 18 NR1I3 splice isoforms and three ESR1 splice isoforms in 260 liver samples derived from AA and EA donors and found variable NR1I3 and ESR1 splice isoform expression in the liver. Multiple linear regression analysis showed that, compared to gene-level expression, isoform-level expression of NR1I3 and ESR1 better predicted the mRNA expression of some CYPs and UGTs, highlighting the importance of isoform-level analyses to enhance our understanding of gene transcriptional regulatory networks controlling the expression of drug-metabolizing enzymes.

Variability in treatment approaches, outcomes, and recurrence patterns after curative intent treatment for primary retroperitoneal sarcomas in Latin America: A report on 333 patients from LATAMSARC collaborative working group

Background and objectiveRetroperitoneal sarcomas (RPS) are rare tumors with several well-defined histologic subtypes. This study aimed to assess the epidemiology, treatment patterns, and outcomes of patients with retroperitoneal sarcoma in Latin America and analyze patterns of recurrence and treatment variations in a large population of patients treated at reference centers. MethodsThis retrospective descriptive study utilized an electronic database of medical records from LATAMSARC (a group studying sarcomas consisting of different centers in various countries in Latin America, including Argentina, Chile, Costa Rica, Mexico, and Peru). Adult patients (≥18 years) with retroperitoneal sarcoma who underwent surgery at six cancer centers in Latin America were included. Key outcomes were practice patterns, overall survival (OS), and progression-free survival (PFS). ResultsIn this retrospective study, 333 patients diagnosed with retroperitoneal sarcomas met the inclusion/exclusion criteria. The cohort consisted of 157 females (42.3 %) and 176 males (57.7 %), with an average age of 53.9 years (SD 13.37). Histology most commonly included Dedifferentiated Liposarcoma (31.5 %), Well-Differentiated Liposarcoma (29.7 %), and Leiomyosarcoma (9.9 %). The tumors predominantly resided in the retroperitoneum (81.7 %) and showed a mean diameter of 23.53 cm (SD 13.8 cm). Surgical interventions varied, with Complete Compartmental Resection performed in 31.2 % of cases and Simple Complete Resection in 29.4 %. Postoperative complications were documented, with Clavien-Dindo Grade ≥3 complications occurring in 16.8 % of patients. An R0/1 resection was obtained in 91.6 % of cases. The ICU admission rate post-surgery was 18.6 %. Adjuvant treatments included radiotherapy in 8.1 %. Local recurrence occurred in 20.7 % of cases, with the majority being ipsilateral. Distal recurrence was observed in 15 patients, predominantly affecting the liver. The mean overall survival was 65.5 months, and disease-free survival was 47.0 months. ConclusionThis study provides information on the epidemiology, treatment patterns, and outcomes of retroperitoneal sarcomas in a group of reference centers in Latin America. These results represent Latin America's largest body of evidence on retroperitoneal sarcomas.

Knowledge-guided Deep Temporal Clustering for Alzheimer's Disease Subtypes in Completed Clinical Trials.

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder with varied patient progression. We aim to test the hypothesis that AD patients can be categorized into subgroups based on differences in progression. We leveraged data from three randomized clinical trials (RCTs) to develop a knowledge-guided, deep temporal clustering (KG-DTC) framework for AD subtyping. This model combined autoencoders for contextual information capture, k-means clustering for representation formation, and clinical outcome classification for clinical knowledge integration. The derived representations, encompassing demographics, APOE genotype, cognitive assessments, brain volumes, and biomarkers, were clustered using the Gaussian Mixture Model to identify AD subtypes. Our novel KG-DTC framework was developed using placebo data from 2,087 AD patients across three solanezumab clinical trials (EXPEDITION, EXPEDITION2, and EXPEDITION3), achieving high performance in outcome prediction and clustering. The KG-DTC model demonstrated superior clustering structures, especially when combined with k-means clustering loss. External validation with independent clinical trial data showed consistent clustering results, with a 0.33 silhouette score for three clusters. The model's stability was confirmed through a leave-one-out approach, with an average adjusted Rand Index around 0.945. Three distinct AD subtypes were identified, each exhibiting unique patterns of cognitive function, neurodegeneration, and amyloid beta levels. Notably, Subtype 3 (S3) showed rapid cognitive decline across multiple clinical measures (e.g., 0.64 in S1 vs. -1.06 in S2 vs. 15.09 in S3 of average ADAS total change score, p<.001). This innovative approach offers promising insights for understanding variability in treatment outcomes and personalizing AD treatment strategies.

Subtyping drug-free first-episode major depressive disorder based on cortical surface area alterations

BackgroundMajor depressive disorder (MDD) is recognized as a complex and heterogeneous metal illness, characterized by diverse clinical symptoms and variable treatment outcomes. Previous studies have repeatedly reported alterations in brain morphology in MDD, but findings vary across sample characteristics. Whether this neurobiological substrate could stratify MDD into more homogeneous clinical subgroups thus improving personalized medicine remains unknown. MethodsWe included 65 drug-free patients with first-episode MDD and 66 healthy controls (HCs) and collected their structural MRI data. We performed the surface reconstruction and calculated cortical surface area using Freesurfer. The surface area of 34 Gy matter regions in each hemisphere based on the Desikan-Killiany atlas were extracted for each participant and subtyping results were obtained with the Louvain community detection algorithm. The demographic and clinical characteristics were then compared between MDD subgroups. ResultsTwo subgroups defined by distinct patterns of cortical surface area were identified in first-episode MDD. Subgroup 1 exhibited a significant reduction in surface area across nearly the entire cortex compared to subgroup 2 and HCs, whereas subgroup 2 demonstrated increased surface area than HCs. Further, subgroup 1 exhibited a higher proportion of females, and higher severity of anxiety symptoms compared to subgroup 2. LimitationsThe relatively small sample size. ConclusionsThis study identified two neurobiologically subgroups with distinct alterations in cortical surface area among drug-free patients with first-episode MDD. Our results highlight the promise of in delineating morphological heterogeneity within MDD, particularly in relation to the severity of anxiety symptoms.

Deciphering colorectal cancer radioresistance and immune microrenvironment: unraveling the role of EIF5A through single-cell RNA sequencing and machine learning.

Radiotherapy (RT) is a critical component of treatment for locally advanced rectal cancer (LARC), though patient response varies significantly. The variability in treatment outcomes is partly due to the resistance conferred by cancer stem cells (CSCs) and tumor immune microenvironment (TiME). This study investigates the role of EIF5A in radiotherapy response and its impact on the CSCs and TiME. Predictive models for preoperative radiotherapy (preRT) response were developed using machine learning, identifying EIF5A as a key gene associated with radioresistance. EIF5A expression was analyzed via bulk RNA-seq and single-cell RNA-seq (scRNA-seq). Functional assays and in vivo experiments validated EIF5A's role in radioresistance and TiME modulation. EIF5A was significantly upregulated in radioresistant colorectal cancer (CRC) tissues. EIF5A knockdown in CRC cell lines reduced cell viability, migration, and invasion after radiation, and increased radiation-induced apoptosis. Mechanistically, EIF5A promoted cancer stem cell (CSC) characteristics through the Hedgehog signaling pathway. Analysis of the TiME revealed that the radiation-resistant group had an immune-desert phenotype, characterized by low immune cell infiltration. In vivo experiments showed that EIF5A knockdown led to increased infiltration of CD8+ T cells and M1 macrophages, and decreased M2 macrophages and Tregs following radiation therapy, thereby enhancing the radiotherapy response. EIF5A contributes to CRC radioresistance by promoting CSC traits via the Hedgehog pathway and modulating the TiME to an immune-suppressive state. Targeting EIF5A could enhance radiation sensitivity and improve immune responses, offering a potential therapeutic strategy to optimize radiotherapy outcomes in CRC patients.

Construction and analysis of a lysosome-dependent cell death score-based prediction model for non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) is the most common type of tumor globally and the leading cause of cancer-related deaths. Although treatment strategies such as immune checkpoint inhibitors and chemotherapy have advanced, the heterogeneity among NSCLC patients results in significant variability in treatment outcomes. Studies have shown that certain patients respond poorly to immune checkpoint inhibitors, indicating that treatment response is closely related to multiple factors. Therefore, it is necessary to develop predictive models to stratify patients based on gene expression and clinical characteristics, aiming for precision therapy.ObjectiveThis study aims to construct a stratified prognostic model for NSCLC patients based on lysosome-dependent cell death (LDCD) scoring by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data. By analyzing the immune-related characteristics of high-risk and low-risk groups, we further explored the impact of cell death patterns on lung cancer and identified potential therapeutic targets.MethodsThis study obtained single-cell RNA sequencing data and gene expression data of NSCLC patients and normal lung tissues from the GEO and TCGA databases. We used R packages such as Seurat and CellChat for data preprocessing and analysis, and performed dimensionality reduction and visualization through Principal Component Analysis (PCA) and UMAP algorithms. LASSO regression analysis was used to construct the predictive model, followed by cross-validation and ROC curve analysis. The model’s effectiveness was validated through survival analysis and immune microenvironment analysis.ResultsThe study showed a significant increase in the proportion of monocytes in NSCLC tissues, suggesting their important role in cancer progression. Cell communication analysis indicated that macrophages, smooth muscle cells, and myeloid cells exhibit strong intercellular communication during cancer progression. Using the constructed prognostic model based on 12 LDCD-related genes, we found significant differences in overall survival and immune microenvironment between the high-risk and low-risk groups.

Development and clinical implementation of an LC-HRMS method for ivacaftor, lumacaftor, tezacaftor and elexacaftor in human plasma and breast milk.

The four cystic fibrosis (CF) transmembrane conductance regulator (CFTR) modulators, ivacaftor, lumacaftor, tezacaftor, and elexacaftor, have revolutionised the treatment of CF by direct action on the protein target behind the disease's development. The aim was to develop and validate a quantification method for these CFTR modulators in plasma and breast milk to better understand inter-patient variability in pharmacokinetics and treatment outcome, including the risk of adverse drug reactions. The ability to monitor CFTR modulators in breast milk enables the estimation of the exposure of breastfed infant, with a potential concern for CFTR modulator-induced liver injury. The analysis was performed on a Thermo Vanquish Flex Binary UHPLC system coupled to a high-resolution mass spectrometer (HRMS), Thermo Q Exactive. The analytes were detected using positive electrospray ionisation in full scan mode. After sample preparation by protein precipitation, the supernatant was injected onto the LC system and the analytes were separated using a Zorbax SB-C18 Rapid Res HPLC column (3.5µm, 4.6 × 75mm). This is the first published method for CFTR modulators in breast milk. The validated quantification range for ivacaftor is 0.0050-10µg/mL with a coefficient of variation < 6% and a mean accuracy of 97-106%; for lumacaftor, tezacaftor, and elexacaftor, the validated quantification range is 0.050-100µg/mL with a coefficient of variation < 8% and a mean accuracy 93-106%. A simple and sensitive quantification method for CFTR modulators has been developed and used for routine analysis of human plasma and breast milk samples since 2022.

Different routes to the same destination? Comparing Diagnostic and Statistical Manual of Mental Disorders, fifth edition Section II- and alternative model of personality disorder-defined borderline personality disorder.

Borderline personality disorder (BPD) is defined by the presence of at least five of nine symptoms in Section II of the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. In the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, Section III Alternative Model of Personality Disorders (AMPD), BPD is defined by deficits in self and/or interpersonal functioning (Criterion A), elevated negative affectivity, and elevated antagonism and/or disinhibition (Criterion B). However, it is unclear if these definitions describe the same people and if the AMPD criteria explain unique variability in treatment outcomes in this population. In a treatment-seeking sample of adult participants diagnosed with BPD according to Section II criteria (n = 65, Mage = 27.60, 70.8% female, 76.9% White), we found a majority (66.2%) would have also received the diagnosis based on AMPD criteria. Those meeting AMPD criteria reported more severe Section II BPD symptoms than those who did not, ps < .02, ds > 0.60, and the presence or severity of Section II fears of abandonment and inappropriate anger uniquely predicted AMPD BPD diagnoses, ps < .03, ORs ≥ 2.31. Changes in AMPD dimensions explained 34% of the variability in change in work/social adjustment (p = .13) and quality of life (p = .22), respectively, over and above changes in Section II symptoms during a novel cognitive-behavioral treatment for BPD. These results suggest that AMPD criteria capture a more severe subset of BPD than Section II criteria and may be important predictors of treatment outcomes. We discuss the potential trade-offs of this shift in diagnosis. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline.

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

Quantitative Expression of Latent Disease Factors in Individuals Associated with Psychopathology Dimensions and Treatment Response.

Psychiatric comorbidity is common in symptom-based diagnoses like autism spectrum disorder (ASD), attention/deficit hyper-activity disorder (ADHD), and obsessive-compulsive disorder (OCD). However, these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level. Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework, we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis. Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention. Four factors, identified as variably co-expressed in each patient, were significantly correlated with distinct symptom domains (r = -0.26-0.53, P < 0.05): behavioral regulation (Factor-1), communication (Factor-2), anxiety (Factor-3), adaptive behaviors (Factor-4). Moreover, we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety, at the degree to which factor expression was significantly predictive of individual symptom scores (r = 0.18-0.5, P < 0.01). Importantly, peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes (r = 0.39, P < 0.05). Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts, which may promote quantitative psychiatric diagnosis and personalized intervention.

Choi IIIB sequelae of septic hip: Etiologically different entities clubbed together?

BackgroundChoi IIIB sequela (pseudarthrosis of femoral neck) is an uncommon complication of septic hip. Only few cases are reported in literature and experience with the entity is limited. Variable pseudarthrosis behaviour and treatment outcomes are reported questioning the mechanical etiology for Choi III sequela. MethodsWe briefly analysed the relevant literature and propose two different pathomechanism for the entity. Three illustrative cases in support of proposed etiology are presented. ResultsThere seems to be two distinct mechanisms of pseudarthrosis formation in Choi IIIB. The originally described mechanism is the articulating head getting dissociated from proximal femur due to a mechanical cause. The second proposed mechanism is the damage of physis and adjoining metaphysis by the septic inflammatory process (Choi II type sequela). ConclusionsIt is important to understand the possible etiologies in Choi IIIB pseudarthrosis as the treatment planning and outcomes may vary. It will also help in prognostication.

Future for cardiogenic shock research.

To discuss future research themes and study design in cardiogenic shock. Cardiogenic shock research faces multiple challenges, hindering progress in understanding and treating this life-threatening condition. Cardiogenic shock's heterogeneous nature poses challenges in patient selection for clinical trials, potentially leading to variability in treatment responses and outcomes. Ethical considerations arise due to the acuity and severity of the condition, posing challenges in obtaining informed consent and conducting randomized controlled trials where time to treatment is pivotal. This review discusses research in this area focusing on the importance of phenotyping patients with cardiogenic shock, based on artificial intelligence, machine learning, and unravel new molecular mechanisms using proteomics and metabolomics. Further, the future research focus in mechanical circulatory support and targeting inflammation is reviewed. Finally, newer trial designs including adaptive platform trials are discussed.

Insights into Therapeutic Response Prediction for Ustekinumab in Ulcerative Colitis Using an Ensemble Bioinformatics Approach.

Optimizing treatment with biological agents is an ideal goal for patients with ulcerative colitis (UC). Recent data suggest that mucosal inflammation patterns and serum cytokine profiles differ between patients who respond and those who do not. Ustekinumab, a monoclonal antibody targeting the p40 subunit of interleukin (IL)-12 and IL-23, has shown promise, but predicting treatment response remains a challenge. We aimed to identify prognostic markers of response to ustekinumab in patients with active UC, utilizing information from their mucosal transcriptome. We performed a prospective observational study of 36 UC patients initiating treatment with ustekinumab. Colonic mucosal biopsies were obtained before treatment initiation for a gene expression analysis using a microarray panel of 84 inflammatory genes. A differential gene expression analysis (DGEA), correlation analysis, and network centrality analysis on co-expression networks were performed to identify potential biomarkers. Additionally, machine learning (ML) models were employed to predict treatment response based on gene expression data. Seven genes, including BCL6, CXCL5, and FASLG, were significantly upregulated, while IL23A and IL23R were downregulated in non-responders compared to responders. The co-expression analysis revealed distinct patterns between responders and non-responders, with key genes like BCL6 and CRP highlighted in responders and CCL11 and CCL22 in non-responders. The ML algorithms demonstrated a high predictive power, emphasizing the significance of the IL23R, IL23A, and BCL6 genes. Our study identifies potential biomarkers associated with ustekinumab response in UC patients, shedding light on its underlying mechanisms and variability in treatment outcomes. Integrating transcriptomic approaches, including gene expression analyses and ML, offers valuable insights for personalized treatment strategies and highlights avenues for further research to enhance therapeutic outcomes for patients with UC.

Proportions and determinants of successful surgical repair of obstetric fistula in low- and middle-income countries: A systematic review and meta-analysis.

Obstetric fistula is a serious and debilitating problem resulting from tissue necrosis on the reproductive and urinary and/or lower gastrointestinal tract organs due to prolonged labor. Primary studies of the treatment of obstetric fistulae report significantly variable treatment outcomes following surgical repair. However, no systematic review and meta-analysis has yet estimated the pooled proportion and identified the determinants of successful obstetric fistula surgical repair. To estimate the proportion and identify the determinants of successful surgical repair of obstetric fistulae in low- and middle-income countries. The protocol was developed and registered at the International Prospective Register of Systematic Reviews (ID CRD42022323630). Searches of PubMed, Embase, CINAHL, Scopus databases, and gray literature sources were performed. All the accessed studies were selected with Covidence, and the quality of the studies was examined. Finally, the data were extracted using Excel and analyzed with R software. This review included 79 studies out of 9337 following the screening process. The analysis reveals that 77.85% (95%CI: 75.14%; 80.56%) of surgical repairs in low and middle-income countries are successful. Women who attain primary education and above, are married, and have alive neonatal outcomes are more likely to have successful repair outcomes. In contrast, women with female genital mutilation, primiparity, a large fistula size, a fistula classification of II and above, urethral damage, vaginal scarring, a circumferential defect, multiple fistulae, prior repair and postoperative complications are less likely to have successful repair outcomes. The proportion of successful surgical repairs of obstetric fistula in low and middle-income countries remains suboptimal. Hence, stakeholders and policymakers must design and implement policies promoting women's education. In addition, fistula care providers need to reach and manage obstetric fistula cases early before complications, like vaginal fibrosis, occur.

Treatment Expectations—You Get What You Expect—and Depression Plays a Role

Positive treatment expectations demonstrably shape treatment outcomes regarding pain and disability in patients with chronic low back pain. However, knowledge about positive and negative treatment expectations as putative predictors of interindividual variability in treatment outcomes is sparse, and the role of other psychological variables of interest, especially of depression as a known predictor of long-term disability, is lacking. We present results of the first prospective study considering expectations in concert with depression in a sample of 200 patients with chronic low back pain undergoing an inpatient interdisciplinary multimodal pain therapy. We analyzed the characteristics of pain and disability, treatment expectation, and depression assessed at the beginning (T0), at the end of (T1), and at 3-month follow-up (T2) of interdisciplinary multimodal pain therapy. Treatment expectations did emerge as a significant predictor of changes in pain intensity and disability, respectively, showing that positive expectations were associated with better treatment outcomes. Mediation analyses revealed a partially mediating effect of treatment expectations on the relation between depression and pain outcomes. PerspectiveThese results expand knowledge regarding the role of treatment expectations in individual treatment outcome trajectories in chronic pain patients, paving the way for much-needed efforts toward optimizing patient expectations and personalized approaches in clinical settings.

Association of patient socioeconomic status with outcomes after palliative treatment for disseminated cancer.

Palliative treatment has been associated with improved quality of life and survival for a wide variety of metastatic cancers. However, it is unclear whether the benefits of palliative treatment are uniformly experienced across the US cancer population. We evaluated patterns and outcomes of palliative treatment based on socioeconomic, sociodemographic and treating facility characteristics. Patients diagnosed between 2008 and 2019 with Stage IV primary cancer of nine organ sites were analyzed in the National Cancer Database. The association between identified variables, and outcomes concerning the administration of palliative treatment were analyzed with multivariable logistic regression and Cox proportional hazard models. Overall 238,995 (23.6%) of Stage IV patients received palliative treatment, which increased over time for all cancers (from 20.7% in 2008 to 25.6% in 2019). Palliative treatment utilization differed significantly by region (West less than Northeast, OR: 0.55 [0.54-0.56], p < 0.001) and insurance payer status (uninsured greater than private insurance, OR: 1.35 [1.32-1.39], p < 0.001). Black race and Hispanic ethnicity were also associated with lower rates of palliative treatment compared to White and non-Hispanics respectively (OR for Blacks: 0.91 [0.90-0.93], p < 0.001 and OR for Hispanics: 0.79 [0.77-0.81] p < 0.001). There are important differences in the utilization of palliative treatment across different populations in the United States. A better understanding of variability in palliative treatment use and outcomes may identify opportunities to improve informed decision making and optimize quality of care at the end-of-life.

Age and mean platelet volume-based nomogram for predicting the therapeutic efficacy of metoprolol in Chinese pediatric patients with vasovagal syncope.

Vasovagal syncope (VVS) is the most common type of orthostatic intolerance in children. We investigated whether platelet-related factors related to treatment efficacy in children suffering from VVS treated with metoprolol. Metoprolol-treated VVS patients were recruited. The median duration of therapy was threemonths. Patients were followed and divided into two groups, treament-effective group and treatment-ineffective group. Logistic and least absolute shrinkage selection operator regressions were used to examine treatment outcome variables. Receiver-operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, and decision curve analyses were used to evaluate the nomogram model. Among the 72 patients who complete the follow-up, treatment-effective group and treatment-ineffective group included 42 (58.3%) and 30 (41.7%) cases, respectively. The patients in the treatment-effective group exhibited higher mean platelet volume (MPV) [(11.0 ± 1.0) fl vs. (9.8 ± 1.0) fl, P < 0.01] and platelet distribution width [12.7% (12.3%, 14.3%) vs. 11.3% (10.2%, 12.2%), P < 0.01]than those in the treatment-ineffective group. The sex ratio was significantly different (P = 0.046). A fit model comprising age [odds ratio (OR) = 0.766, 95% confidence interval (CI) = 0.594-0.987] and MPV (OR = 5.613, 95% CI = 2.297-13.711) might predict therapeutic efficacy. The area under the curve of the ROC and PR curves was computed to be 0.85 and 0.9, respectively. The P value of the Hosmer-Lemeshow test was 0.27. The decision curve analysis confirmed that managing children with VVS based on the predictive model led to a net advantage ranging from 0.01 to 0.58. The nomogram is convenient for clinical applications. A novel nomogram based on age and MPV can predict the therapeutic benefits of metoprolol in children with VVS.

Understanding multiple sclerosis as a disease spectrum: above and below the clinical threshold.

Research in multiple sclerosis (MS) has long been predicated on clinical groupings that do not reflect the underlying biologic heterogeneity apparent within patient populations. This review explicates the various levels of explanation through which the spectrum of disease is described and investigated both above and below the clinical threshold of detection, as framed by the topographical model of MS, to help advance a cogent mechanistic framework. Contemporary evidence has amended the view of MS as consisting of sequential disease phases in favor of a spectrum of disease with an admixture of interdependent and dynamic pathobiological axes driving tissue injury and progression. Recent studies have shown the presence of acute and compartmentalized inflammation and mechanisms of neurodegeneration beginning early and evolving throughout the disease continuum. Still, the gap between the understanding of immunopathologic processes in MS and the tools used to measure relevant molecular, laboratory, radiologic, and clinical metrics needs attention to enable better prognostication of disease and monitoring for changes along specific pathologic axes and variable treatment outcomes. Aligning on a consistently-applied mechanistic framework at distinct levels of explanation will enable greater precision across bench and clinical research, and inform discourse on drivers of disability progression and delivery of care for individuals with MS.

Polymorphism of Transporter Genes Contributes to Variations in Treatment Outcomes and Adverse Events in Platinum Based- Chemotherapy- Treated Oral Cancer Patients

Polymorphism of Transporter Genes Contributes to Variations in Treatment Outcomes and Adverse Events in Platinum Based- Chemotherapy- Treated Oral Cancer Patients