The STRAT-PARK cohort: A personalized initiative to stratify Parkinson's disease

The STRAT-PARK initiative aims to provide a platform for stratifying Parkinson's disease (PD) into biological subtypes, using a bottom-up, multidisciplinary biomarker-based and data-driven approach. PD is a heterogeneous entity, exhibiting high interindividual clinicopathological variability. This diversity suggests that PD may encompass multiple distinct biological entities, each driven by different molecular mechanisms. Molecular stratification and identification of disease subtypes is therefore a key priority for understanding and treating PD. STRAT-PARK is a multi-center longitudinal cohort aiming to recruit a total of 2000 individuals with PD and neurologically healthy controls from Norway and Canada, for the purpose of identifying molecular disease subtypes. Clinical assessment is performed annually, whereas biosampling, imaging, and digital and neurophysiological phenotyping occur every second year. The unique feature of STRAT-PARK is the diversity of collected biological material, including muscle biopsies and platelets, tissues particularly useful for mitochondrial biomarker research. Recruitment rate is ∼150 participants per year. By March 2023, 252 participants were included, comprising 204 cases and 48 controls. STRAT-PARK is a powerful stratification initiative anticipated to become a global research resource, contributing to personalized care in PD.

Using blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification

INTRODUCTION

Blood protein biomarkers demonstrate potential for Alzheimer's disease (AD) diagnosis. Limited studies examine the molecular changes in AD blood cells.

METHODS

Bulk RNA-sequencing of blood cells was performed on AD patients of Chinese descent (n = 214 and 26 in the discovery and validation cohorts, respectively) with normal controls (n = 208 and 38 in the discovery and validation cohorts, respectively). Weighted gene co-expression network analysis (WGCNA) and deconvolution analysis identified AD-associated gene modules and blood cell types. Regression and unsupervised clustering analysis identified AD-associated genes, gene modules, cell types, and established AD classification models.

RESULTS

WGCNA on differentially expressed genes revealed 15 gene modules, with 6 accurately classifying AD (areas under the receiver operating characteristics curve [auROCs] > 0.90). These modules stratified AD patients into subgroups with distinct disease states. Cell-type deconvolution analysis identified specific blood cell types potentially associated with AD pathogenesis.

DISCUSSION

This study highlights the potential of blood transcriptome for AD diagnosis, patient stratification, and mechanistic studies.

HIGHLIGHTS

We comprehensively analyze the blood transcriptomes of a well-characterized Alzheimer's disease cohort to identify genes, gene modules, pathways, and specific blood cells associated with the disease. Blood transcriptome analysis accurately classifies and stratifies patients with Alzheimer's disease, with some gene modules achieving classification accuracy comparable to that of the plasma ATN biomarkers. Immune-associated pathways and immune cells, such as neutrophils, have potential roles in the pathogenesis and progression of Alzheimer's disease.

Distinct differences of vertical phytoplankton community structure in mainstream and a tributary bay of the Three Gorges Reservoir, China

The vertical distribution of phytoplankton plays a crucial role in shaping the dynamics and structure of aquatic communities. In highly dynamic reservoir systems, water level fluctuations significantly affect the physiochemical conditions and the phytoplankton community. However, the specific effects on the vertical characteristics of phytoplankton between the mainstream and the tributary bay of the reservoir remain unstudied. This study investigated the vertical aspects of phytoplankton density, biomass, α and β diversity through monthly sampling over two years in the mainstream (Chang Jiang, CJ) and a tributary bay (Xiang Xi, XX) of the Three Gorges Reservoir in China. Phytoplankton density and biomass were significantly higher in XX, indicating an increased risk of algal blooms in the tributary. The phytoplankton community in CJ showed more stable species-environment relationships, a lower Shannon index and a higher evenness index, suggesting a relatively simple structure and a more uniform distribution of phytoplankton among different water layers. Conversely, XX showed greater differences between water layers (higher β diversity), with significant negative correlations with water level and positive correlations with DO difference, dissolved silica (DSi) difference, and stratification. Peak phytoplankton density and biomass, as well as high β diversity in XX, occurred during periods of decreased water levels with strong stratification in spring and summer. A structural equation model complemented by path analysis revealed that a decrease in water level could increase β diversity either directly through internal processes with extended residence time or indirectly by modifying stratification and the vertical distribution of DSi in XX. Therefore, a proposed water quality management strategy for XX was to increase the water level or reduce β diversity by implementing artificial mixing during stratification periods. Overall, this study lies in its comprehensive investigation of the vertical characteristics of the phytoplankton community in both the mainstream and the tributary bay of the Three Gorges Reservoir, elucidating the significant impact of water level fluctuations and providing insights for targeted water quality management strategies in the tributary bay to mitigate potential ecological impacts.

Regulating diagnosis-Molecular and regulatory sub-stratifications of lung cancer treatment

The sociology of diagnosis has shown that diagnosis not only serves to label the underlying cause of disease but also to provide access to services and resources. Elaborating on this double-affordance of diagnosis, this article examines how precision medicine reconfigures diagnosis as a label and as a process in regulatory and clinical settings. Reporting from an ethnographic case study of the introduction of immunotherapy for lung cancer, the paper unfolds the uncertainties involved in dissecting diagnosis into layers and examines the efforts and negotiations it takes to enable these layers to work both as clinical entities and regulative entities with the purpose of delineating access to treatment. I suggest that the work of subdividing diseases into molecularly defined categories for the purpose of delineating treatment-eligible populations can be labelled 'diagnostic sub-stratification' and argue that it is pertinent to understand the political capacity of this strategy. Diagnostic sub-stratification involves a push of diagnosis from the clinic 'up' into the regulatory system and 'out' into the laboratories, obscuring who is accountable for the diagnostic categories employed to define patients' treatment access.

Vertical eDNA distribution of cold-water fishes in response to environmental variables in stratified lake

In summer, the survival zones of cold-water species are predicted to narrow by both increasing water temperatures from the surface and by expanding hypoxic zones from the lake bottom. To examine how the abundance of cold-water fishes changes along environmental gradients, we assessed the vertical environmental DNA (eDNA) distributions of three salmonid species which may have different water temperature tolerances during both stratification and turnover periods using quantitative PCR (qPCR). In addition, we examined on the vertical distribution of diverse fish fauna using an eDNA metabarcoding assay. The results suggested that the kokanee salmon (Oncorhynchus nerka) eDNA were abundant in deep, cold waters. On the other hand, rainbow trout (O. mykiss) eDNA were distributed uniformly throughout the water column, suggesting that they may have high water-temperature tolerance compared with kokanee salmon. The eDNA concentrations of masu salmon (O. masou) were below the detection limit (i.e., <10 copies μL-1) at all stations and depths and hence could not be quantified during stratification. Together with the finding that the eDNA distributions of other prey fish species were also constrained vertically in species-specific ways, our results suggest that climate change will result in substantial changes in the vertical distributions of lake fish species and thus affect their populations and interactions.

Gender inequalities in unpaid public work: Retention, stratification and segmentation in the volunteer leadership of charities in England and Wales

While gender inequalities in employment (paid public work) and domestic and reproductive labour (unpaid private work) are a prominent focus within the sociological literature, gender inequalities in volunteering (unpaid public work) have received much less scholarly attention. We analyse a unique longitudinal dataset of volunteer leaders, that follows through time every individual to have served as a board member (trustee) for a charity in England and Wales between 2010 and 2023, to make three foundational contributions to our understanding of gender inequalities in unpaid public work. First, the salience of vertical gender stratification and horizontal gender segmentation in trusteeship shows that gendered inequalities in work extend to public work in general-encompassing unpaid public work, and not only paid public work. In terms of gender segmentation, we find that women are over-represented as trustees in a small number of fields of charitable activity but under-represented across the majority of fields. In terms of gender stratification, we find that women are under-represented on the boards of the largest charities; under-represented as chairs of trustee boards; and particularly under-represented as chairs of the largest charities. Second, the dynamics underlying gendered differences in unpaid public work, which show higher rates of resignation for women trustees, resonate with research on paid employment which emphasises the importance of attrition to an understanding of how gendered inequalities in work are reproduced. This means that increasing the retention of women, not only the recruitment of women, becomes central to the policy agenda. Third, we show that there has been a decline in gender stratification and gender segmentation in trusteeship since 2010. This decline over time in gendered inequalities in unpaid public work provides an interesting counterpoint to influential research documenting a 'stall' in the reduction of gendered inequalities in paid employment.

Deep analysis of skin molecular heterogeneities and their significance on the precise treatment of patients with psoriasis

Background

Psoriasis is a highly heterogeneous autoinflammatory disease. At present, heterogeneity in disease has not been adequately translated into concrete treatment options. Our aim was to develop and verify a new stratification scheme that identifies the heterogeneity of psoriasis by the integration of large-scale transcriptomic profiles, thereby identifying patient subtypes and providing personalized treatment options whenever possible.

Methods

We performed functional enrichment and network analysis of upregulated differentially expressed genes using microarray datasets of lesional and non-lesional skin samples from 250 psoriatic patients. Unsupervised clustering methods were used to identify the skin subtypes. Finally, an Xgboost classifier was utilized to predict the effects of methotrexate and commonly prescribed biologics on skin subtypes.

Results

Based on the 163 upregulated differentially expressed genes, psoriasis patients were categorized into three subtypes (subtypes A-C). Immune cells and proinflammatory-related pathways were markedly activated in subtype A, named immune activation. Contrastingly, subtype C, named stroma proliferation, was enriched in integrated stroma cells and tissue proliferation-related signaling pathways. Subtype B was modestly activated in all the signaling pathways. Notably, subtypes A and B presented good responses to methotrexate and interleukin-12/23 inhibitors (ustekinumab) but inadequate responses to tumor necrosis factor-α inhibitors and interleukin-17A receptor inhibitors. Contrastly, subtype C exhibited excellent responses to tumor necrosis factor-α inhibitors (etanercept) and interleukin-17A receptor inhibitors (brodalumab) but not methotrexate and interleukin-12/23 inhibitors.

Conclusions

Psoriasis patients can be assorted into three subtypes with different molecular and cellular characteristics based on the heterogeneity of the skin's immune cells and the stroma, determining the clinical responses of conventional therapies.

PCOS stratification for precision diagnostics and treatment

Globally, polycystic ovarian syndrome (PCOS) affects approximately 10% of fertile women, leading to great health and economic burden. PCOS is a heterogenous illness that can cause infertility, irregular menstrual cycles, acne, and hirsutism, among other symptoms. The clinical diagnosis is primarily a diagnosis of exclusion if one or more of the three primary symptoms, namely, oligo- or anovulation, hyperandrogenism, and polycystic ovarian morphology, are present. Obesity and PCOS are often coexisting disorders that may be bidirectionally causally related. Phenotypic heterogeneity throughout the reproductive lifespan, such as the overlap of PCOS symptoms with regular fluctuations in a woman's menstrual cycle and metabolism during the menarche and menopausal transition, further complicates diagnosis. PCOS etiology is mostly unknown and complex, likely due to the fact that it is a group of disorders with overlapping metabolic and reproductive problems. Evidence-based, common, standardized guidelines for PCOS diagnosis and treatment are urgently needed. Genomics and clinical data from populations across diverse ages and ethnicities are urgently needed to build efficient machine learning models for the stratification of PCOS. PCOS subtype-specific strategies for early screening, an accurate diagnosis, and management throughout life will optimize healthcare resources and reduce unnecessary testing. This will pave the way for women to be able to take the best possible care of their own health using the latest clinical expertise combined with their unique needs and preferences.

Unraveling the mystery: a Mendelian randomized exploration of gut microbiota and different types of obesity

Background

Numerous studies have demonstrated the influence of gut microbiota on the development of obesity. In this study, we utilized Mendelian randomization (MR) analysis to investigate the gut microbiota characteristics among different types of obese patients, aiming to elucidate the underlying mechanisms and provide novel insights for obesity treatment.

Methods

Two-sample multivariable Mendelian randomization (MR) analysis was employed to assess causal relationships between gut microbiota and various obesity subtypes. Gut microbiota data were obtained from the international consortium MiBioGen, and data on obese individuals were sourced from the Finnish National Biobank FinnGen. Eligible single-nucleotide polymorphisms (SNPs) were selected as instrumental variables. Various analytical methods, including inverse variance weighted (IVW), MR-Egger regression, weighted median, MR-RAPS, and Lasso regression, were applied. Sensitivity analyses for quality control included MR-Egger intercept tests, Cochran's Q tests, and leave-one-out analyses and others.

Results

Mendelian randomization studies revealed distinct gut microbiota profiles among European populations with different obesity subtypes. Following multivariable MR analysis, we found that Ruminococcaceae UCG010 [Odds Ratio (OR): 0.842, 95% confidence interval (CI): 0.766-0.926, Adjusted P value: 0.028] independently reduced the risk of obesity induced by excessive calorie intake, while Butyricimonas [OR: 4.252, 95% CI: 2.177-8.307, Adjusted P value: 0.002] independently increased the risk of medication-induced obesity. For localized adiposity, Pasteurellaceae [OR: 0.213, 95% CI: 0.115-0.395, Adjusted P value: <0.001] acted as a protective factor. In the case of extreme obesity with alveolar hypoventilation, lactobacillus [OR: 0.724, 95% CI: 0.609-0.860, Adjusted P value: 0.035] reduced the risk of its occurrence. Additionally, six gut microbiota may have potential roles in the onset of different types of obesity. Specifically, the Ruminococcus torques group may increase the risk of its occurrence. Desulfovibrio and Catenabacterium may serve as protective factors in the onset of Drug-induced obesity. Oxalobacteraceae, Actinomycetaceae, and Ruminiclostridium 9, on the other hand, could potentially increase the risk of Drug-induced obesity. No evidence of heterogeneity or horizontal pleiotropy among SNPs was found in the above studies (all P values for Q test and MR-Egger intercept > 0.05).

Conclusion

Gut microbiota abundance is causally related to obesity, with distinct gut microbiota profiles observed among different obesity subtypes. Four bacterial species, including Ruminococcaceae UCG010, Butyricimonas, Pasteurellaceae and lactobacillus independently influence the development of various types of obesity. Probiotic and prebiotic supplementation may represent a novel approach in future obesity management.

Stratification of neurogenic bladder risk in spina bifida using the urinary peptidome

Neurogenic bladder poses a major morbidity in children with spina bifida (SB), and videourodynamic studies (VUDS) are used to stratify this risk. This small-scale pilot study utilized current mass-spectrometry-based proteomic approaches to identify peptides or proteins in urine that may differentiate children at high risk of developing renal complications from a neurogenic bladder. Twenty-two urine samples of which nine had high bladder pressure storage that put the upper urinary tract at risk, while 13 with a lower risk for renal compromise were analyzed. More than 1,900 peptides across all 22 samples were quantified, and 115 peptides differed significantly (P < 0.05) between the two groups. Using machine learning approaches five peptides that showed the greatest differences between these two clinical categories were used to build a classifier. We tested this classifier by blind analysis of an additional six urine samples and showed that it correctly assigned the unknown samples in their proper risk category. These promising results indicate that a urinary screening test based on peptides could be performed on a regular basis to stratify the neurogenic bladder into low or high-risk categories. Expanding this work to larger cohorts as well as across a broad spectrum of urodynamics outcomes may provide a useful diagnostic test for neurogenic bladder.NEW & NOTEWORTHY This approach could help risk stratify the neurogenic bladder in patients with spina bifida and could allow us to safely defer on up to 1/3 of urodynamic studies. These pilot data justify a larger trial before this approach becomes a clinical tool.

New investigational drugs to treat Sjogren's syndrome: lessons learnt from immunology

INTRODUCTION

Sjögren's syndrome is a heterogeneous autoimmune condition that impairs quality of life because of dryness, fatigue, pain, and systemic involvements. Current treatment largely depends on empirical evidence, with no effective therapy approved. Clinical trials on targeted drugs often fail to report efficacy due to common factors.

AREAS COVERED

This review summarizes the pathogenesis and what caused the failure of new investigational drugs in clinical trials, highlighting solutions for more effective investigations, with greater consistency between research outcomes, clinical use, and patient needs.

EXPERT OPINION

Unlinked pathobiology with symptoms resulted in misidentified targets and disappointing trials. Useful stratification tools are necessary for the heterogeneous SS patients. Composite endpoints or improvements in ESSDAI scores are needed, considering the high placebo response, and the unbalance between symptom burden and disease activity. Compared to classic biologics, targeted cell therapy will be a more promising field of investigation in the coming years.

Validity and reliability of the Persian version of the STarT musculoskeletal tool

BACKGROUND

The Subgrouping for Targeted Treatment (STarT) musculoskeletal (MSK) tool stratifies patients with MSK disorders (MSDs) into prognostic categories based on poor outcomes.

PURPOSE

This study aimed at investigating the validity and reliability of the Persian STarT MSK tool in people suffering from painful MSDs in Iran.

METHODS

A total of 593 subjects with painful MSDs including neck, shoulder, low back, knee, and multisite pain received and completed the STarT MSK tool, visual analog scale (VAS), EuroQol five-dimensions three-levels questionnaire (EQ-5D-3 L), short form-36 health survey questionnaire (SF-36), and Örebro musculoskeletal pain screening questionnaire (ÖMPSQ) in the first visit. To examine test-retest reliability, 234 patients completed the STarT MSK tool 2 days after the initial visit.

RESULTS

In this study, 139 (23.5%), 266 (44.9%), and 188 (31.7%) participants were classified as low-, medium-, and high-risk groupings for poor outcomes, respectively. Spearman's correlation coefficient showed a strong relationship among Persian STarT MSK tool and EQ-5D-3 L (-0.78), SF-36 (-0.76), and OMPSQ (0.70). The results of known-group validity indicated that this tool could distinguish among the participants in different risk subgroups based on the scores of the ÖMPSQ, VAS, SF36, and EQ-5D-5 L (p < .001). No ceiling and floor effects were observed. Cronbach's alpha and intra-class correlation coefficient (ICC2,1) were acceptable (0.71) and excellent (0.98), respectively.

CONCLUSION

The Persian version of STarT MSK tool has shown to be a valid and reliable instrument to stratify people with painful MSDs into low-, medium-, and high-risk subgroups based on persistent pain disability.

International survey of radiocephalic arteriovenous fistula: ISRAF survey

AIMS

The objective of this survey was to encompass the full scope of international practice, entailing all technical, non-technical, preoperative stratification and functional maturation (FM) of RCAVF.

METHODS

The survey contained n = 19 questionnaires with n = 46 variables completed by n = 85 providers from n = 21 nations across n = 5 continents. The numerical values were subjected to mean with standard error whereas the nominal data to a non-parametric (Kruskal-Wallis & Spearman correlation test) and analysis of variance (ANOVA). The test of homogeneity, & probability was reported 95% confidence intervals (CI) alongside error plots. Furthermore, a decision and higher attribute tree model was constructed based on current survey for higher FM in RCAVF.

RESULTS

FM is independently associated with volume of surgeon per year (procedures performed) (p < 0.01) [High Volume: 73% (95% CI, 68-77%) versus Average volume: 63% (95% CI, 59-66%) vs Low volume: 56% (95% CI, 51-61%)]. FM increased by 8% with every 20 more procedures per group of surgeons on end point of FM. Amongst continents: Australia, America, Asia and South America demonstrated higher FM to Africa & Europe (p < 0.05). UK possessed a lower FM 58% (95% CI, 48-68%) in comparison to the world & Europe respectively [65% (95% CI, 61-70%) vs 61% (95% CI, 58--65%)]. There was a positive causal link between angle of anastomosis at 30-76° (p < 0.01), longitudinal & S-shaped incision & arteriotomy length of 3 & 4 mm to higher FM (p < 0.05).

CONCLUSION

FM in RCAVF is independently & incrementally associated with the volume of surgeon per year. There is a diverse inclusion, exclusion and technical approach in RCAVF creation. This survey advocates the importance of international collaboration and/or registry in assimilation, consolidate and development of consensus.

Utilization of Primary Healthcare Services in Patients with Multimorbidity According to Their Risk Level by Adjusted Morbidity Groups: A Cross-Sectional Study in Chamartín District (Madrid)

Patients with multimorbidity have increased and more complex healthcare needs, posing their management a challenge for healthcare systems. This study aimed to describe their primary healthcare utilization and associated factors. A population-based cross-sectional study was conducted in a Spanish basic healthcare area including all patients with chronic conditions, differentiating between having multimorbidity or not. Sociodemographic, functional, clinical and service utilization variables were analyzed, stratifying the multimorbid population by the Adjusted Morbidity Groups (AMG) risk level, sex and age. A total of 6036 patients had multimorbidity, 64.2% being low risk, 28.5% medium risk and 7.3% high risk. Their mean age was 64.1 years and 63.5% were women, having on average 3.5 chronic diseases, and 25.3% were polymedicated. Their mean primary care contacts/year was 14.9 (7.8 with family doctors and 4.4 with nurses). Factors associated with primary care utilization were age (B-coefficient [BC] = 1.15;95% Confidence Interval [CI] = 0.30-2.01), female sex (BC = 1.04; CI = 0.30-1.78), having a caregiver (BC = 8.70; CI = 6.72-10.69), complexity (B-coefficient = 0.46; CI = 0.38-0.55), high-risk (B-coefficient = 2.29; CI = 1.26-3.32), numerous chronic diseases (B-coefficient = 1.20; CI = 0.37-2.04) and polypharmacy (B-coefficient = 5.05; CI = 4.00-6.10). This study provides valuable data on the application of AMG in multimorbid patients, revealing their healthcare utilization and the need for a patient-centered approach by primary care professionals. These results could guide in improving coordination among professionals, optimizing multimorbidity management and reducing costs derived from their extensive healthcare utilization.

Exploring the intestinal ecosystem: from gut microbiota to associations with subtypes of inflammatory bowel disease

Objective

Significant differences have been discovered between subtypes of Crohn's disease (CD) and ulcerative colitis (UC). The role of gut microbiota in promoting the onset of UC and CD is established, but conclusions regarding subtype-specific analyses remain limited.

Methods

This study aims to explore the influence of gut microbiota on subtypes of UC and CD, offering novel insights into the pathogenesis and treatment of UC and CD.Two-sample Mendelian randomization (MR) analysis was employed to examine the causal relationship between subtypes of UC and CD and gut microbiota composition. Gut microbiota data were sourced from the International Consortium MiBioGen, while UC and CD data were obtained from FINNGEN. Eligible single nucleotide polymorphisms (SNPs) were selected as instrumental variables. Multiple analytical approaches such as inverse variance-weighted (IVW), MR-Egger regression, weighted median, weighted mode, and MR-RAPS were utilized. Sensitivity analyses including MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were conducted for quality control. Subsequently, we employed multivariable IVW, MR-Egger, weighted median, and LASSO regression methods to identify independently significant genera or families and conducted sensitivity analyses.

Results

We have determined that Hungatella, Acidaminococcaceae, and 15 other microbial taxa act as protective factors for various CD and UC subtypes, while Terrisporobacter, Anaerostipes, and 23 other microbial taxa are associated with increased risk for different CD and UC subtypes. Furthermore, through multivariable MR analysis, we have identified significant genera or families with independent effects.

Conclusion

Our study confirms a causal relationship between dysbiosis of gut microbiota and the occurrence of CD and UC subtypes. Furthermore, it validates etiological distinctions among different subtypes of CD and UC. A novel approach to adjunctive therapy involving distinct UC or CD subtypes may involve the use of probiotics and represents a potential avenue for future treatments.

ctDNA quantification improves estimation of outcomes in patients with high-grade osteosarcoma: a translational study from the OS2006 trial

BACKGROUND

Osteosarcoma stratification relies on clinical parameters and histological response. We developed a new personalized stratification using less invasive circulating tumor DNA (ctDNA) quantification.

PATIENTS AND METHODS

Plasma from patients homogeneously treated in the prospective protocol OS2006, at diagnosis, before surgery and end of treatment, were sequenced using low-passage whole-genome sequencing (lpWGS) for copy number alteration detection. We developed a prediction tool including ctDNA quantification and known clinical parameters to estimate patients' individual risk of event.

RESULTS

ctDNA quantification at diagnosis (diagCPA) was evaluated for 183 patients of the protocol OS2006. diagCPA as a continuous variable was a major prognostic factor, independent of other clinical parameters, including metastatic status [diagCPA hazard ratio (HR) = 3.5, P = 0.002 and 3.51, P = 0.012, for progression-free survival (PFS) and overall survival (OS)]. At the time of surgery and until the end of treatment, diagCPA was also a major prognostic factor independent of histological response (diagCPA HR = 9.2, P < 0.001 and 11.6, P < 0.001, for PFS and OS). Therefore, the addition of diagCPA to metastatic status at diagnosis or poor histological response after surgery improved the prognostic stratification of patients with osteosarcoma. We developed the prediction tool PRONOS to generate individual risk estimations, showing great performance ctDNA quantification at the time of surgery and the end of treatment still required improvement to overcome the low sensitivity of lpWGS and to enable the follow-up of disease progression.

CONCLUSIONS

The addition of ctDNA quantification to known risk factors improves the estimation of prognosis calculated by our prediction tool PRONOS. To confirm its value, an external validation in the Sarcoma 13 trial is underway.

Analysis of the Larissa Heart Failure Risk Score: Predictive Value in 9207 Patients Hospitalized for Heart Failure from a Single Center

Early risk stratification is of outmost clinical importance in hospitalized patients with heart failure (HHF). We examined the predictive value of the Larissa Heart Failure Risk Score (LHFRS) in a large population of HHF patients from the Cleveland Clinic. A total of 13,309 admissions for heart failure (HF) from 9207 unique patients were extracted from the Cleveland Clinic's electronic health record system. For each admission, components of the 3-variable simple LHFRS were obtained, including hypertension history, myocardial infarction history, and red blood cell distribution width (RDW) ≥ 15%. The primary outcome was a HF readmission and/or all-cause mortality at one year, and the secondary outcome was all-cause mortality at one year of discharge. For both outcomes, all variables were statistically significant, and the Kaplan-Meier curves were well-separated and in a consistent order (Log-rank test p-value < 0.001). Higher LHFRS values were found to be strongly related to patients experiencing an event, showing a clear association of LHFRS with this study outcomes. The bootstrapped-validated area under the curve (AUC) for the logistic regression model for each outcome revealed a C-index of 0.64 both for the primary and secondary outcomes, respectively. LHFRS is a simple risk model and can be utilized as a basis for risk stratification in patients hospitalized for HF.

Comparison of competing-risks model with angiogenic factors in midgestation screening for preterm growth-related neonatal morbidity

OBJECTIVES

First, to evaluate the predictive performance for preterm growth-related neonatal morbidity of high soluble fms-like tyrosine kinase-1 (sFLT-1) / placental growth factor (PlGF) ratio or low PlGF at mid-gestation, and second, to compare the performance of the high sFLT-1/PlGF ratio or low PlGF with that of the competing risks model for small for gestational age (SGA), utilizing a combination of maternal risk factors, sonographic estimated fetal weight (EFW) and uterine artery pulsatility index (UtA-PI).

METHODS

This was a prospective observational study in women attending for a routine hospital visit at 19 to 24 weeks' gestation in two maternity hospitals in England. The visit included recording of maternal demographic characteristics and medical history, carrying out an ultrasound scan and measuring serum PlGF and sFLT-1. The primary outcome was delivery <32 and <37 weeks' gestation of SGA neonate with birth weight <10th or <3rd percentile for gestational age, combined with neonatal unit (NNU) admission for ≥48 hours or a composite of major neonatal morbidity. The detection rates in screening by either PlGF <10th percentile, sFLT-1/PlGF ratio >90th percentile and the competing risks model for SGA were estimated and they were compared using McNemar's test.

RESULTS

In the study population of 40241 women prediction of preterm growth-related neonatal morbidity provided by the competing risks model for SGA was superior to that of screening by low PlGF concentration or high sFlt-1/PlGF concentration ratio. For example, at screen positive rate (SPR) of 10.0%, as defined by the sFLT-1/ PlGF ratio >90th percentile, the competing risks model predicted 70.1% (95% CI 61.0 - 79.2) of SGA <10th percentile and 76.9% (67.6-86.3) of SGA <3rd percentile with NNU admission for ≥48 hours delivered <32 weeks gestation and these were significantly higher than the respective values of 35.0% (25.6-44.6) and 35.9% (25.3 - 46.5), achieved by the application of the sFLT-1/ PlGF ratio >90th percentile (p<0.0001 for both). The respective values for SGA with major neonatal morbidity were 73.8% (64.4-83.2), 77.9% (68.0-87.8), 38.1% (27.7-48.5) and 39.7% (28.1-51.3) (Both p<0.0001).

CONCLUSION

At mid-gestation, the prediction of growth-related neonatal morbidity by the competing risks model for SGA is superior to that of high sFlt-1/PlGF ratio or low PlGF. This article is protected by copyright. All rights reserved.

Nfia Is Critical for AII Amacrine Cell Production: Selective Bipolar Cell Dependencies and Diminished ERG

The nuclear factor one (NFI) transcription factor genes Nfia, Nfib, and Nfix are all enriched in late-stage retinal progenitor cells, and their loss has been shown to retain these progenitors at the expense of later-generated retinal cell types. Whether they play any role in the specification of those later-generated fates is unknown, but the expression of one of these, Nfia, in a specific amacrine cell type may intimate such a role. Here, Nfia conditional knockout (Nfia-CKO) mice (both sexes) were assessed, finding a massive and largely selective absence of AII amacrine cells. There was, however, a partial reduction in type 2 cone bipolar cells (CBCs), being richly interconnected to AII cells. Counts of dying cells showed a significant increase in Nfia-CKO retinas at postnatal day (P)7, after AII cell numbers were already reduced but in advance of the loss of type 2 CBCs detected by P10. Those results suggest a role for Nfia in the specification of the AII amacrine cell fate and a dependency of the type 2 CBCs on them. Delaying the conditional loss of Nfia to the first postnatal week did not alter AII cell number nor differentiation, further suggesting that its role in AII cells is solely associated with their production. The physiological consequences of their loss were assessed using the ERG, finding the oscillatory potentials to be profoundly diminished. A slight reduction in the b-wave was also detected, attributed to an altered distribution of the terminals of rod bipolar cells, implicating a role of the AII amacrine cells in constraining their stratification.SIGNIFICANCE STATEMENT The transcription factor NFIA is shown to play a critical role in the specification of a single type of retinal amacrine cell, the AII cell. Using an Nfia-conditional knockout mouse to eliminate this population of retinal neurons, we demonstrate two selective bipolar cell dependencies on the AII cells; the terminals of rod bipolar cells become mis-stratified in the inner plexiform layer, and one type of cone bipolar cell undergoes enhanced cell death. The physiological consequence of this loss of the AII cells was also assessed, finding the cells to be a major contributor to the oscillatory potentials in the electroretinogram.

Evaluation of Seed Germination of Six Rare Stipa Species following Low Temperature Stress (Cryopreservation)

Cryopreservation is one way to preserve rare, endangered species. However, during the cryopreservation process, plant cells undergo considerable stress, which may lead to cell death. In our work, orthodox Stipa seeds of six rare species were cryopreserved: S. sareptana, S. ucrainica, S. tirsa, S. dasyphylla, S. adoxa, and S. pulcherríma. Short-term cryopreservation (14 days) stimulated germination of all Stipa species studied. Prolonged cryopreservation (70 days and more) decreased the germination of all Stipa seeds except S. sareptana. The decrease in germination progressed over time as a result of the cumulative stress of cryopreservation rather than the initial stress. To stimulate germination, seeds were stratified and treated with GA3, KNO3, NaOH, and H2O2. After four years of seed cryopreservation, it was possible to obtain seedlings of all the Stipa species studied with 30 days of stratification and 180 days of germination. After five years of cryopreservation and seed treatment with 30% NaOH for one hour, the best germination was obtained in S. adoxa and S. pulcherrima. After treatment with 5% H2O2 for 20 min, the best germination was obtained in S. sareptana, S. ucrainica, and S. dasyphylla. S. sareptana seeds germinated in all the aforementioned experiments. S. sareptana has a non-deep physiological dormancy and is the most widespread and drought-tolerant Stipa species studied. The best habitat adaptation and stress tolerance correlated with this species'cryotolerance. S. sareptana was recommended for further cryopreservation, while storage protocols for the other Stipa species studied need further improvements.

Type 1 diabetes in the pancreas: A histological perspective

AIMS

This review aims to introduce research in the pancreas to a broader audience. The pancreas is a heterocrine gland residing deep within our abdominal cavity. It is the home to our islets, which play a pivotal role in regulating metabolic homeostasis. Due to its structure and location, it is an impossible organ to study, in molecular detail, in living humans, and yet, understanding the pancreas is critical if we aim to characterise the immunopathology of type 1 diabetes (T1D) and one day prevent the triggering of the autoimmune attack associated with ß-cell demise.

METHODS

Over a 100 years ago, we began studying pancreatic histology using cadaveric samples and clever adaptations to microscopes. As histologists, some may say nothing much has changed. Nevertheless, our microscopes can now interrogate multiple proteins at molecular resolution. Images of pancreas sections are no longer constrained to a single field of view and can capture a thousands and thousands of cells. AI-image-analysis packages can analyse these massive data sets offering breakthrough findings.

CONCLUSION

This narrative review will provide an overview of pancreatic anatomy, and the importance of research focused on the pancreas in T1D. It will range from histological breakthroughs to briefly discussing the challenges associated with characterising the organ. I shall briefly introduce a selection of the available global biobanks and touch on the distinct pancreatic endotypes that differ immunologically and in ß-cell behaviour. Finally, I will introduce the idea of developing a collaborative tool aimed at developing a cohesive framework for characterising heterogeneity and stratifying endotypes in T1D more readily.

Vertical habitat stratification in Aedes triseriatus and Aedes hendersoni (Diptera: Culicidae): complications associated with sloped and flood-prone landscapes

For the sibling mosquito species Aedes triseriatus Say and Ae. hendersoni Cockerell, success of offspring is highly dependent on oviposition site selection by gravid females, and the dynamics of that selection process have been the subject of much investigation. Patterns of vertical oviposition stratification were examined in this study by placing basal and canopy level oviposition traps in relatively steep and flat terrains, as well as in flood-prone riverbank vs upland settings. Strong stratification existed in both steep and flat terrains, with Ae. triseriatus favoring basal ovitraps and Ae. hendersoni favoring canopy ovitraps. Contrary to expectations, Ae. hendersoni displayed even more marked preference for canopy ovitraps in steeper terrain. Both species avoided basal ovitraps in flood-prone locations, where Ae. triseriatus oviposited only in canopy ovitraps and greatly outnumbered Ae. hendersoni in canopy ovitraps. In contrast, the expected stratification was found in nearby upland habitats.

Sample size and power determination for multiparameter evaluation in nonlinear regression models with potential stratification

Sample size and power determination are crucial design considerations for biomedical studies intending to formally test the effects of key variables on an outcome. Other known prognostic factors may exist, necessitating the use of techniques for covariate adjustment when conducting this evaluation. Moreover, the main interest often includes assessing the impact of more than one variable on an outcome, such as multiple treatments or risk factors. Regression models are frequently employed for these purposes, formalizing this assessment as a test of multiple regression parameters. But, the presence of multiple variables of primary interest and correlation between covariates can complicate sample size/power calculation. Given the paucity of available sample size formulas for this context, these calculations are often performed via simulation, which can be both time-consuming as well as demanding extensive probability modeling. We propose a simpler, general approach to sample size and power determination that may be applied when testing multiple parameters in commonly used regression models, including generalized linear models as well as ordinary and stratified versions of the Cox and Fine-Gray models. Through both rigorous simulations and theoretical derivations, we demonstrate the formulas' accuracy in producing sample sizes that will meet the type I error rate and power specifications of the study design.

A novel staging system derived from natural language processing of pathology reports to predict prognostic outcomes of pancreatic cancer: a retrospective cohort study

OBJECTIVE

To construct a novel tumor-node-morphology (TNMor) staging system derived from natural language processing (NLP) of pathology reports to predict outcomes of pancreatic ductal adenocarcinoma.

METHOD

This retrospective study with 1657 participants was based on a large referral center and The Cancer Genome Atlas Program (TCGA) dataset. In the training cohort, NLP was used to extract and screen prognostic predictors from pathology reports to develop the TNMor system, which was further evaluated with the tumor-node-metastasis (TNM) system in the internal and external validation cohort, respectively. Main outcomes were evaluated by the log-rank test of Kaplan-Meier curves, the concordance index (C-index), and the area under the receiver operating curve (AUC).

RESULTS

The precision, recall, and F1 scores of the NLP model were 88.83, 89.89, and 89.21%, respectively. In Kaplan-Meier analysis, survival differences between stages in the TNMor system were more significant than that in the TNM system. In addition, our system provided an improved C-index (internal validation, 0.58 vs. 0.54, P <0.001; external validation, 0.64 vs. 0.63, P <0.001), and higher AUCs for 1, 2, and 3-year survival (internal validation: 0.62 vs. 0.54, P <0.001; 0.64 vs. 0.60, P= 0.017; 0.69 vs. 0.62, P= 0.001; external validation: 0.69 vs. 0.65, P= 0.098; 0.68 vs. 0.64, P= 0.154; 0.64 vs. 0.55, P= 0.032, respectively). Finally, our system was particularly beneficial for precise stratification of patients receiving adjuvant therapy, with an improved C-index (0.61 vs. 0.57, P <0.001), and higher AUCs for 1-year, 2-year, and 3-year survival (0.64 vs. 0.57, P <0.001; 0.64 vs. 0.58, P <0.001; 0.67 vs. 0.61, P <0.001; respectively) compared with the TNM system.

CONCLUSION

These findings suggest that the TNMor system performed better than the TNM system in predicting pancreatic ductal adenocarcinoma prognosis. It is a promising system to screen risk-adjusted strategies for precision medicine.

Cell Line-Based Human Bladder Organoids with Bladder-like Self-Organization-A New Standardized Approach in Bladder Cancer Research

Three-dimensional tumor models have gained significant importance in bladder cancer (BCa) research. Organoids consisting of different cell types better mimic solid tumors in terms of 3D architecture, proliferation, cell-cell interaction and drug responses. We developed four organoids from human BCa cell lines with fibroblasts and smooth muscle cells of the bladder, aiming to find models for BCa research. The organoids were characterized in terms of cytokeratins, vimentin, α-actin and KI67 by immunoreactivity. Further, we studied ligand-dependent activation of the Wnt/β-catenin pathway and investigated the responses to anti-tumor therapies. The organoids mimicked the structure of an inverse bladder wall, with outside urothelial cells and a core of supportive cells. The cytokeratin staining patterns and proliferation rate were in conjunction with the origins of the BCa cells. RT-112 even showed stratification of the epithelium. Treatment with Wnt10B led to increased β-catenin (active) levels in high-grade organoids, but not in low-grade BCa cells. Doxorubicin treatment resulted in clearly reduced viability (10-30% vs. untreated). In contrast, the effectivity of radiotherapy depended on the proliferation status of BCa cells. In conclusion, cell-line-based organoids can form bladder-like structures and reproduce in vivo features such as urothelial differentiation and stratification. Thus, they can be useful tools for functional studies in BCa and anti-cancer drug development.

Vertical Distribution of Fruit Flies (Diptera: Drosophilidae) in Deciduous Forests in the Center of European Russia

Research of Diptera in temperate forests has demonstrated uneven vertical distributions of insects. In this study, we examined the vertical distribution, seasonal fluctuations, and species diversity of Drosophilidae species in the Mordovia State Reserve. This research marks the first exploration of drosophilid vertical stratification in the European part of Russia. Using traps, we collected flies in four deciduous forest sites between early June and mid-September in 2020. A total of 27,151 individuals from 10 genera and 34 drosophilid species were identified, with 6 species from 4 genera being new to the Republic of Mordovia. Drosophila obscura Fll. and Scaptodrosophila rufifrons Lw. were the most abundant species in traps. The total highest number of drosophilid flies (10,429 individuals) was captured at a height of 1.5 m, while the lowest number (5086 individuals) was recorded at 12 m. The average number of flies was 6240 and 5387 individuals at heights of 7.5 m and 3.5 m, respectively. However, the prevalence of drosophilid numbers at the 1.5-m height was not constant during the season. We found that in the second part of July the total fly counts at heights of 7.5 m and 12 m exceeded those at 1.5 m. We have described five different types of vertical distribution of drosophilids throughout the season, which differs markedly in mycetobionts and xylosaprobionts ecological groups. Species diversity demonstrated variations across different sites and tiers during the season, with peak diversity observed in June and September.

Hukou Status and Cognitive Function Among Older Chinese Adults: Does Support from Friends Matter?

OBJECTIVES

This study examined whether older Chinese adults with different types of hukou status (government household registration system) exhibited different cognitive outcomes and whether receiving support from friends, an under-appreciated resource, helped mitigate the negative impacts of agricultural hukou status on cognitive health disparities.

METHODS

Using nationally representative data from the China Longitudinal Aging Social Survey, this study tested these relationships with well-validated measures.

RESULTS

Our results showed that older Chinese adults with agricultural hukou were more likely to have worse cognitive function than those with non-agricultural hukou. Further, friend support characteristics moderated the association between hukou status and cognitive function, whereby having better friend support was related to a weaker negative effect of agricultural hukou status on cognitive function.

DISCUSSION

The findings suggested that agricultural hukou status reflects the effects of accumulated disadvantage across the life course with negative consequences for late-life cognition. The cognitive health disparities between agricultural and non-agricultural residents may be reduced in the context of a higher level of friend support, supporting a stress buffering hypothesis.

Metabolomic biomarkers in autism: identification of complex dysregulations of cellular bioenergetics

Autism Spectrum Disorder (ASD or autism) is a phenotypically and etiologically heterogeneous condition. Identifying biomarkers of clinically significant metabolic subtypes of autism could improve understanding of its underlying pathophysiology and potentially lead to more targeted interventions. We hypothesized that the application of metabolite-based biomarker techniques using decision thresholds derived from quantitative measurements could identify autism-associated subpopulations. Metabolomic profiling was carried out in a case-control study of 499 autistic and 209 typically developing (TYP) children, ages 18-48 months, enrolled in the Children's Autism Metabolome Project (CAMP; ClinicalTrials.gov Identifier: NCT02548442). Fifty-four metabolites, associated with amino acid, organic acid, acylcarnitine and purine metabolism as well as microbiome-associated metabolites, were quantified using liquid chromatography-tandem mass spectrometry. Using quantitative thresholds, the concentrations of 4 metabolites and 149 ratios of metabolites were identified as biomarkers, each identifying subpopulations of 4.5-11% of the CAMP autistic population. A subset of 42 biomarkers could identify CAMP autistic individuals with 72% sensitivity and 90% specificity. Many participants were identified by several metabolic biomarkers. Using hierarchical clustering, 30 clusters of biomarkers were created based on participants' biomarker profiles. Metabolic changes associated with the clusters suggest that altered regulation of cellular metabolism, especially of mitochondrial bioenergetics, were common metabolic phenotypes in this cohort of autistic participants. Autism severity and cognitive and developmental impairment were associated with increased lactate, many lactate containing ratios, and the number of biomarker clusters a participant displayed. These studies provide evidence that metabolic phenotyping is feasible and that defined autistic subgroups can lead to enhanced understanding of the underlying pathophysiology and potentially suggest pathways for targeted metabolic treatments.

Reduced PDE4D7 in prostate cancer correlates with genomic downregulation within the upstream PDE4D coding region

Aim

PDE4D7 expression is significantly associated with prostate cancer (PCa) progression, representing an attractive prognostic biomarker. We sought to determine whether other genes in the PDE4D coding region were associated.

Patients & methods

RNA from biopsy punch samples of resected tumor tissue was analyzed via RNA sequencing. RT-qPCR was used to determine PDE4D7 score.

Results

Numerous genomic sequences within the PDE4D coding region on Chr5q12 revealed similar mRNA expression profiles to PDE4D7. PART1 had a significantly similar expression pattern to PDE4D7 across samples, correlating with disease progression. However, many other genes also exhibited matched expression to PDE4D7, including miRNAs and lncRNAs.

Conclusion

These novel PDE4D7-associated genes, many of which are previously uncharacterized in cancer, represent putative PCa biomarkers and could have mechanistic roles in PCa progression.

Interpreting Randomized Controlled Trials

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

Stratification and Adaptation of Malaria Control Interventions in Chad

Malaria remains the leading cause of morbidity and mortality in Chad. The World Health Organization (WHO) has recommended that endemic countries stratify malaria to guide interventions. Thus, the Republic of Chad has initiated a stratification process based on malaria incidence with the aim of defining transmission risk and proposing interventions. We collected routine malaria data from health facilities from 2017-2021, the national survey on malaria indicators, the entomological data of NMCP operational research, the demographic and health surveys, and remote sensing of environmental data. Stratification was based on the adjusted incidence of malaria to guide interventions. The adjusted incidence of malaria was, on average, 374 cases per 1000 people in the country. However, it varied according to health districts. Health districts were stratified into very low malaria incidence (n = 25), low malaria incidence (n = 20), moderate malaria incidence (n = 46) and high malaria incidence (n = 38). Micro-stratification in health districts with very low incidence was carried out to identify districts with incidence <10 cases per 1000 person with a view to a malaria pre-elimination programme. Appropriate malaria control interventions were proposed based on the strata identified. Stratification enables the country to target interventions to accelerate the reduction of the burden caused by malaria with a pre-elimination goal.

Childhood emotional abuse and adult mental health at the intersection of social relationship and education

BACKGROUND

Previous research has established that childhood emotional abuse has long-term, negative consequences for adult mental health. Yet, less is known about the ways that social relationship and education intersect to shape the link between childhood emotional abuse and mental health in adulthood.

AIM

The current study aims to examine whether perceived quality of social relationships moderates the association between childhood emotional abuse and adult mental health. Moreover, it assesses how the moderating effect of perceived quality of social relationship differs across levels of education.

METHOD

The current study analyzes data from the 2012 Korean General Social Survey, a nationally representative sampling of Korean adults. It uses OLS regression models.

RESULTS

Childhood emotional abuse is positively associated with depression and psychological distress in adulthood. However, perceived quality of social relationships mitigates the positive association of childhood emotional abuse with depression and psychological distress. Further, this buffering effect of perceived quality of social relationships operates only for individuals with less than or equal to a high school education, but not for individuals with college education or more.

CONCLUSION

The results lend support to the resource substitution thesis, suggesting that positive perceptions of social relationship act as a protective factor against childhood emotional abuse for individuals with lower levels of education.

Predicting diabetic foot ulceration using routinely collected data in a foot clinic. What level of prognostic accuracy can be achieved?

This study aimed to investigate the efficacy of using routinely collected clinical data in predicting the risk of diabetic foot ulcer (DFU). The first objective was to develop a prognostic model based on the most important risk factors objectively selected from a set of 39 clinical measures. The second objective was to compare the prediction accuracy of the developed model against that of a model based on only the 3 risk factors that were suggested in the systematic review and meta-analyses study (PODUS). In a cohort study, a set of 12 continuous and 27 categorical data from patients (n = 203 M/F:99/104) who attended a specialised diabetic foot clinic were collected at baseline. These patients were then followed-up for 24 months during which 24 (M/F:17/7) patients had DFU. Multivariate logistic regression was used to develop a prognostic model using the identified risk factors that achieved p < 0.2 based on univariate logistic regression. The final prognostic model included 4 risk factors (Adjusted-OR [95% CI]; p) in total. Impaired sensation (116.082 [12.06-1117.287]; p = 0.000) and presence of callus (6.257 [1.312-29.836]; p = 0.021) were significant (p < 0.05), while having dry skin (5.497 [0.866-34.89]; p = 0.071) and Onychomycosis (6.386 [0.856-47.670]; p = 0.071) that stayed in the model were not significant. The accuracy of the model with these 4 risk factors was 92.3%, where sensitivity and specificity were 78.9%, and 94.0% respectively. The 78.9% sensitivity of our prognostic 4-risk factor model was superior to the 50% sensitivity that was achieved when the three risk factors proposed by PODUS were used. Also our proposed model based on the above 4 risk factors showed to predict the DFU with higher overall prognostic accuracy. These findings have implications for developing prognostic models and clinical prediction rules in specific patient populations to more accurately predict DFU.

Improved prediction of 5-year mortality by updating the chronic related score for risk profiling in the general population: lessons from the italian region of Lombardy

Objective

The aim of this study was to improve the performance of the Chronic Related Score (CReSc) in predicting mortality and healthcare needs in the general population.

Methods

A population-based study was conducted, including all beneficiaries of the Regional Health Service of Lombardy, Italy, aged 18 years or older in January 2015. Each individual was classified as exposed or unexposed to 69 candidate predictors measured before baseline, updated to include four mental health disorders. Conditions independently associated with 5-year mortality were selected using the Cox regression model on a random sample including 5.4 million citizens. The predictive performance of the obtained CReSc-2.0 was assessed on the remaining 2.7 million citizens through discrimination and calibration.

Results

A total of 35 conditions significantly contributed to the CReSc-2.0, among which Alzheimer's and Parkinson's diseases, dementia, heart failure, active neoplasm, and kidney dialysis contributed the most to the score. Approximately 36% of citizens suffered from at least one condition. CReSc-2.0 discrimination performance was remarkable, with an area under the receiver operating characteristic curve of 0.83. Trends toward increasing short-term (1-year) and long-term (5-year) rates of mortality, hospital admission, hospital stay, and healthcare costs were observed as CReSc-2.0 increased.

Conclusion

CReSC-2.0 represents an improved tool for stratifying populations according to healthcare needs.

On variance estimation of target population created by inverse probability weighting

Inverse probability weighting (IPW) is frequently used to reduce or minimize the observed confounding in observational studies. IPW creates a pseudo-sample by weighting each individual by the inverse of the conditional probability of receiving the treatment level that he/she has actually received. In the pseudo-sample there is no variation among the multiple individuals generated by weighting the same individual in the original sample. This would reduce the variability of the data and therefore bias the variance estimate in the target population. Conventional variance estimation methods for IPW estimators generally ignore this underestimation and tend to produce biased estimates of variance. We here propose a more reasonable method that incorporates this source of variability by using parametric bootstrapping based on intra-stratum variability estimates. This approach firstly uses propensity score stratification and intra-stratum standard deviation to approximate the variability among multiple individuals generated based on a single individual whose propensity score falls within the corresponding stratum. The parametric bootstrapping is then used to incorporate the target variability by re-generating outcomes after adding a random error term to the original data. The performance of the proposed method is compared with three existing methods including the naïve model-based variance estimator, the nonparametric bootstrap variance estimator, and the robust variance estimator in the simulation section. An example of patients with sarcopenia is used to illustrate the implementation of the proposed approach. According to the results, the proposed approach has desirable statistical properties and can be easily implemented using the provided R code.

The economics of early inequality

We examine three recent frameworks that attempt to explain early inequality. One explanation involves the emergence of dense and predictable resource patches in the Holocene, together with differential asset accumulation and inheritance by individuals or households. In this view, agriculture and pastoralism led to greater inequality because farmland and animal herds were readily inherited. Another explanation involves the distinction between ideal free and ideal despotic population distributions, together with factors that could trigger a transition from the former to the latter. We offer a third framework based on economic concepts. In our view, inequality initially arose across locations (insider-outsider inequality) and reflected geographical differences in resource endowments at those locations. As population densities increased, the barriers to individual migration across locations included fewer kinship linkages and the use of force by insiders to exclude outsiders. These barriers became important with the transition from mobile to sedentary foraging and predate agriculture. Insider-outsider inequality was followed by stratification within settlements (elite-commoner inequality), which arose at still higher population densities. We see these three theoretical approaches as distinct but complementary. While they overlap, each emphasizes some phenomena and processes ignored by the other two. This article is part of the theme issue 'Evolutionary ecology of inequality'.

One Step Closer to Coatings Applications Utilizing Self-Stratification: Effect of Rheology Modifiers

Self-stratification of model blends of colloidal spheres has recently been demonstrated as a method to form multifunctional coatings in a single pass. However, practical coating formulations are complex fluids with upward of 15 components. Here, we investigate the influence of three different rheology modifiers (RMs) on the stratification of a 10 wt % 7:3 w:w blend of 270 and 96 nm anionic latex particles that do not stratify without RM. However, addition of a high molar mass polysaccharide thickener, xanthan gum, raises the viscosity and corresponding Péclet number enough to achieve small-on-top stratification as demonstrated by atomic force microscopy (AFM) measurements. Importantly, this was possible due to minimal particle-rheology modifier interactions, as demonstrated by the bulk rheology. In contrast, Carbopol 940, a microgel-based RM, was unable to achieve small-on-top stratification despite a comparable increase in viscosity. Instead, pH-dependent interactions with latex particles lead to either laterally segregated structures at pH 3 or a surface enrichment of large particles at pH 8. Strong RM-particle interactions are also observed when the triblock associative RM HEUR10kC12 is used. Here, small-on-top, large-enhanced, and randomly mixed structures were observed at respectively 0.01, 0.1, and 1 wt % HEUR10kC12. Combining rheology, dynamic light scattering, and AFM results allows the mechanisms behind the nonmonotonic stratification in the presence of associative RMs to be elucidated. Our results highlight that stratification can be predicted and controlled for RMs with weak particle interactions, while a strong RM-particle interaction may afford a wider range of stratified structures. This takes a step toward successfully harnessing stratification in coatings formulations.

Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool

Purpose

Identify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering.

Material and methods

All patients were treated using radiation therapy for OPC. Dose-volume histograms of organs-at-risk were extracted from patients' treatment plans. Symptom ratings were collected via the MD Anderson Symptom Inventory (MDASI) given weekly during, and 6 months post-treatment. Drymouth, trouble swallowing, mucus, and vocal dysfunction were selected for analysis in this study. Patient stratifications were obtained by applying Bayesian Mixture Models with three components to patient's dose histograms for relevant organs. The clusters with the highest total mean doses were translated into dose thresholds using rule mining. Patient stratifications were compared against Tumor staging information using multivariate likelihood ratio tests. Model performance for prediction of moderate/severe symptoms at 6 months was compared against normal tissue complication probability (NTCP) models using cross-validation.

Results

A total of 349 patients were included for long-term symptom prediction. High-risk clusters were significantly correlated with outcomes for severe late drymouth (p <.0001, OR = 2.94), swallow (p = .002, OR = 5.13), mucus (p = .001, OR = 3.18), and voice (p = .009, OR = 8.99). Simplified clusters were also correlated with late severe symptoms for drymouth (p <.001, OR = 2.77), swallow (p = .01, OR = 3.63), mucus (p = .01, OR = 2.37), and voice (p <.001, OR = 19.75). Proposed cluster stratifications show better performance than NTCP models for severe drymouth (AUC.598 vs.559, MCC.143 vs.062), swallow (AUC.631 vs.561, MCC.20 vs -.030), mucus (AUC.596 vs.492, MCC.164 vs -.041), and voice (AUC.681 vs.555, MCC.181 vs -.019). Simplified dose thresholds also show better performance than baseline models for predicting late severe ratings for all symptoms.

Conclusion

Our results show that leveraging the 3-D dose histograms from radiation therapy plan improves stratification of patients according to their risk of experiencing long-term severe radiation associated symptoms, beyond existing NTPC models. Our rule-based method can approximate our stratifications with minimal loss of accuracy and can proactively identify risk factors for radiation-associated toxicity.

Prediction of adverse perinatal outcome at midgestation

OBJECTIVES

First, to investigate the association between adverse neonatal outcomes and birth weight and gestational age at delivery. Second, to describe the distribution of adverse neonatal outcomes within different risk strata derived by a population stratification scheme based on the midgestation risk assessment for small-for-gestational-age (SGA) neonates using a competing-risks model.

METHODS

This was a prospective observational cohort study in women with a singleton pregnancy attending a routine hospital visit at 19 + 0 to 23 + 6 weeks' gestation. The incidence of neonatal unit (NNU) admission for ≥ 48 h was evaluated within different birth-weight-percentile subgroups. The pregnancy-specific risk of delivery with SGA < 10th percentile at < 37 weeks was estimated by the competing-risks model for SGA, combining maternal factors and the likelihood functions of Z-score of sonographically estimated fetal weight and uterine artery pulsatility index multiples of the median. The population was stratified into six risk categories: > 1 in 4, > 1 in 10 to ≤ 1 in 4, > 1 in 30 to ≤ 1 in 10, > 1 in 50 to ≤ 1 in 30, > 1 in 100 to ≤ 1 in 50 and ≤ 1 in 100. The outcome measures were admission to the NNU for a minimum of 48 h, perinatal death and major neonatal morbidity. The incidence of each adverse outcome was estimated in each risk stratum.

RESULTS

In the study population of 40 241 women, 0.8%, 2.5%, 10.8%, 10.2%, 19.0% and 56.7% were in the risk strata > 1 in 4, > 1 in 10 to ≤ 1 in 4, > 1 in 30 to ≤ 1 in 10, > 1 in 50 to ≤ 1 in 30, > 1 in 100 to ≤ 1 in 50 and ≤ 1 in 100, respectively. Women in higher-risk strata were more likely to deliver a baby that suffered an adverse outcome. The incidence of NNU admission for ≥ 48 h was highest in the > 1 in 4 risk stratum (31.9% (95% CI, 26.9-36.9%)) and it gradually decreased until the ≤ 1 in 100 risk stratum (5.6% (95% CI, 5.3-5.9%)). The mean gestational age at delivery in SGA cases with NNU admission for ≥ 48 h was 32.9 (95% CI, 32.2-33.7) weeks for risk stratum > 1 in 4 and progressively increased to 37.5 (95% CI, 36.8-38.2) weeks for risk stratum ≤ 1 in 100. The incidence of NNU admission for ≥ 48 h was highest for neonates with birth weight below the 1st percentile (25.7% (95% CI, 23.0-28.5%)) and decreased progressively until the 25th to < 75th percentile interval (5.4% (95% CI, 5.1-5.7%)). Preterm SGA neonates < 10th percentile had significantly higher incidence of NNU admission for ≥ 48 h compared with preterm non-SGA neonates (48.7% (95% CI, 45.0-52.4%) vs 40.9% (95% CI, 38.5-43.3%); P < 0.001). Similarly, term SGA neonates < 10th percentile had significantly higher incidence of NNU admission for ≥ 48 h compared with term non-SGA neonates (5.8% (95% CI, 5.1-6.5%) vs 4.2% (95% CI, 4.0-4.4%); P < 0.001).

CONCLUSIONS

Birth weight has a continuous association with the incidence of adverse neonatal outcomes, which is affected by gestational age. Pregnancies at high risk of SGA, estimated at midgestation, are also at increased risk for adverse neonatal outcomes. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Deep stratification by transcriptome molecular characters for precision treatment of patients with systemic lupus erythematosus

OBJECTIVES

To leverage the high clinical heterogeneity of systemic lupus erythematosus (SLE), we developed and validated a new stratification scheme by integrating genome-scale transcriptomic profiles to identify patient subtypes sharing similar transcriptomic markers and drug targets.

METHODS

A normalized compendium of transcription profiles was created from peripheral blood mononuclear cells (PBMCs) of 1046 SLE patients and 86 healthy controls (HCs), covering an intersection of 13 689 genes from six microarray datasets. Upregulated differentially expressed genes were subjected to functional and network analysis in which samples were grouped using unsupervised clustering to identify patient subtypes. Then, clustering stability was evaluated by the stratification of six integrated RNA-sequencing datasets using the same method. Finally, the Xgboost classifier was applied to the independent datasets to identify factors associated with treatment outcomes.

RESULTS

Based on 278 upregulated DEGs of the transcript profiles, SLE patients were classified into three subtypes (subtype A-C) each with distinct molecular and cellular signatures. Neutrophil activation-related pathways were markedly activated in subtype A (named NE-driving), whereas lymphocyte and IFN-related pathways were more enriched in subtype B (IFN-driving). As the most severe subtype, subtype C [NE-IFN-dual-driving (Dual-driving)] shared functional mechanisms with both NE-driving and IFN-driving, which was closely associated with clinical features and could be used to predict the responses of treatment.

CONCLUSION

We developed the largest cohesive SLE transcriptomic compendium for deep stratification using the most comprehensive microarray and RNA sequencing datasets to date. This result could guide future design of molecular diagnosis and the development of stratified therapy for SLE patients.

Personalized stratification of pregnancy care for small for gestational age neonates from biophysical markers at midgestation

BACKGROUND

Antenatal identification of pregnancies at high risk of delivering small for gestational age neonates may improve the management of the condition and reduce the associated adverse perinatal outcomes. In a series of publications, we have developed a new competing-risks model for small for gestational age prediction, and we demonstrated that the new approach has a superior performance to that of the traditional methods. The next step in shaping the appropriate management of small for gestational age is the timely assessment of these high-risk pregnancies according to an antenatal stratification plan.

OBJECTIVE

This study aimed to demonstrate the stratification of pregnancy care based on individual patient risk derived from the application of the competing-risks model for small for gestational age that combines maternal factors with sonographic estimated fetal weight and uterine artery pulsatility index at midgestation.

STUDY DESIGN

This was a prospective observational study of 96,678 singleton pregnancies undergoing routine ultrasound examination at 19 to 24 weeks of gestation, which included recording of estimated fetal weight and measurement of uterine artery pulsatility index. The competing-risks model for small for gestational age was used to create a patient-specific stratification curve capable to define a specific timing for a repeated ultrasound examination after 24 weeks. We examined different stratification plans with the intention of detecting approximately 80%, 85%, 90%, and 95% of small for gestational age neonates with birthweight <3rd and <10th percentiles at any gestational age at delivery until 36 weeks; all pregnancies would be offered a routine ultrasound examination at 36 weeks.

RESULTS

The stratification of pregnancy care for small for gestational age can be based on a patient-specific stratification curve. Factors from maternal history, low estimated fetal weight, and increased uterine artery pulsatility index shift the personalized risk curve toward higher risks. The degree of shifting defines the timing for assessment for each pregnancy. If the objective of our antenatal plan was to detect 80%, 85%, 90%, and 95% of small for gestational age neonates at any gestational age at delivery until 36 weeks, the median (range) proportions (percentages) of population examined per week would be 3.15 (1.9-3.7), 3.85 (2.7-4.5), 4.75 (4.0-5.4), and 6.45 (3.7-8.0) for small for gestational age <3rd percentile and 3.8 (2.5-4.6), 4.6 (3.6-5.4), 5.7 (3.8-6.4), and 7.35 (3.3-9.8) for small for gestational age <10th percentile, respectively.

CONCLUSION

The competing-risks model provides an effective personalized continuous stratification of pregnancy care for small for gestational age which is based on individual characteristics and biophysical marker levels recorded at the midgestation scan.

Community form, function and phylogenetic diversity respond differently across microhabitat and recovery gradients

Research Highlight: Hoenle, P. O., Staab, M., Donoso, D. A., Argoti, A., & Blüthgen, N. (2023). Stratification and recovery time jointly shape ant functional reassembly in a neotropical forest. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13896. Space, time and abiotic variation are primary axes across investigations of community ecology and disturbed ecosystems offer tractable systems for assessing their relative impact. While recovering forests can act as isolated case studies in understanding community assembly, it is not well understood how individual microhabitats respond to recovery and ultimately shape community attributes. Hoenle et al. (2023) leverage the ubiquity and microhabitat-specific diversity of ants across a gradient from active agricultural sites to old-growth forest and assess how recovery and stratification together shape communities. The authors find distinct stratification across phylogenetic, functional and trait diversity as forest recovery time increases, while also recovering unique recovery trajectories contingent on trait sampling. While stratified, phylogenetic and functional diversity did not increase along this recovery gradient. Ten out of 13 sampled traits were jointly influenced by both stratification and recovery time. In contrast to intuitive predictions, a majority of trait means converged throughout the recovery period. Results highlight the multifaceted nature of recovery-based community assembly and the capacity of multidimensional sampling to uncover surprising patterns in ecologically diverse lineages.

The use of the World Guidelines for Falls Prevention and Management's risk stratification algorithm in predicting falls in The Irish Longitudinal Study on Ageing (TILDA)

BACKGROUND

the aim of this study was to retrospectively operationalise the World Guidelines for Falls Prevention and Management (WGFPM) falls risk stratification algorithm using data from The Irish Longitudinal Study on Ageing (TILDA). We described how easy the algorithm was to operationalise in TILDA and determined its utility in predicting falls in this population.

METHODS

participants aged ≥50 years were stratified as 'low risk', 'intermediate' or 'high risk' as per WGFPM stratification based on their Wave 1 TILDA assessments. Groups were compared for number of falls, number of people who experienced one or more falls and number of people who experienced an injury when falling between Wave 1 and Wave 2 (approximately 2 years).

RESULTS

5,882 participants were included in the study; 4,521, 42 and 1,309 were classified as low, intermediate and high risk, respectively, and 10 participants could not be categorised due to missing data. At Wave 2, 17.4%, 43.8% and 40.5% of low-, intermediate- and high-risk groups reported having fallen, and 7.1%, 18.8% and 18.7%, respectively, reported having sustained an injury from falling.

CONCLUSION

the implementation of the WGFPM risk assessment algorithm was feasible in TILDA and successfully differentiated those at greater risk of falling. The high number of participants classified in the low-risk group and lack of differences between the intermediate and high-risk groups may be related to the non-clinical nature of the TILDA sample, and further study in other samples is warranted.

The role of pharmacogenomics in precision psychiatry

The field of psychiatry is facing an important paradigm shift in the provision of clinical care and mental health service organization toward personalization and integration of multimodal data science. This approach, termed precision psychiatry, aims at identifying subgroups of patients more prone to the development of a certain phenotype, such as symptoms or severe mental disorders (risk detection), and/or to guide treatment selection. Pharmacogenomics and computational psychiatry are two fundamental tools of precision psychiatry, which have seen increasing levels of integration in clinical settings. Here we present a brief overview of these two applications of precision psychiatry in clinical settings.

Dose-escalating ruxolitinib for refractory hemophagocytic lymphohistiocytosis

Background

Hemophagocytic lymphohistiocytosis (HLH) is a severe disorder characterized by excessive secretion of cytokines. Even with the recommended HLH-94/2004 regimen, over 30% of patients remain refractory to frontline therapy or relapse after an initial response, leading to poor clinical outcomes. Ruxolitinib, a JAK1/2 inhibitor targets key cytokines in HLH, has shown promising therapeutic effects. However, there has been little attention given to patients who do not respond to ruxolitinib and whether an escalating dose can provide a resolution.

Methods

This study analyzed eight HLH patients who received dose-escalating ruxolitinib who had previously failed to respond to the general dose. The efficacy and safety were mainly analyzed.

Results

Overall, four out of eight (50%) patients achieved better remission after dose escalation. Two patients who only showed improvement with the general dose achieved complete remission (CR) after dose escalation, and the other two patients also achieved CR after dose escalation when they did not respond to the general dose. The median time to achieve the best overall response was 18.5 days (IQR 13.25-23.75 days). There was no correlation of treatment outcome with blood count, liver function, LDH, cytokines, ferritin levels, NK cell activity, or the time to initiation of ruxolitinib and maximum dosage. The etiology of HLH (p=0.029) and level of sCD25 (p=0.021) correlated with treatment response to dose-escalating ruxolitinib. The area of sCD25 under the ROC curve was 0.8125 (95% CI 0.5921 to 1.033, p=0.035) when using 10,000 pg/ml as the cut-off value for predicting therapeutic effects. After a median follow-up of 159 days, two patients died, and the estimated 2-month overall survival rate was 75%. Adverse effects possibly related to the dose-escalating of ruxolitinib included two cases of extremity pain and one of aminotransferase increased. No grade 3 or higher adverse events were reported.

Conclusion

This is the first comprehensive study on the use of dose-escalating ruxolitinib in HLH. Ruxolitinib at an escalated dose represent a viable and relatively safe solution for managing refractory HLH. The levels of sCD25 (with a cut-off of 10000pg/ml) can serve as an indicator for early consideration of chemotherapy during treatment.

Clinical, Histological, and Molecular Prognostic Factors in Childhood Medulloblastoma: Where Do We Stand?

Medulloblastomas, highly aggressive neoplasms of the central nervous system (CNS) that present significant heterogeneity in clinical presentation, disease course, and treatment outcomes, are common in childhood. Moreover, patients who survive may be diagnosed with subsequent malignancies during their life or could develop treatment-related medical conditions. Genetic and transcriptomic studies have classified MBs into four subgroups: wingless type (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4, with distinct histological and molecular profiles. However, recent molecular findings resulted in the WHO updating their guidelines and stratifying medulloblastomas into further molecular subgroups, changing the clinical stratification and treatment management. In this review, we discuss most of the histological, clinical, and molecular prognostic factors, as well the feasibility of their application, for better characterization, prognostication, and treatment of medulloblastomas.

Association of Peripheral Inflammatory Biomarkers and Growth Factors Levels with Sex, Therapy and Other Clinical Factors in Schizophrenia and Patient Stratification Based on These Data

Multiple lines of evidence are known to confirm the pro-inflammatory state of some patients with schizophrenia and the involvement of inflammatory mechanisms in the pathogenesis of psychosis. The concentration of peripheral biomarkers is associated with the severity of inflammation and can be used for patient stratification. Here, we analyzed changes in serum concentrations of cytokines (IL-1β, IL-2, IL-4, IL-6, IL-10, IL-21, APRIL, BAFF, PBEF/Visfatin, IFN-α, and TNF-α) and growth/neurotrophic factors (GM-CSF, NRG1-β1, NGF-β, and GDNF) in patients with schizophrenia in an exacerbation phase. IL-1β, IL-2, IL-4, IL-6, BAFF, IFN-α, GM-CSF, NRG1-β1, and GDNF increased but TNF-α and NGF-β decreased in schizophrenia compared to healthy individuals. Subgroup analysis revealed the effect of sex, prevalent symptoms, and type of antipsychotic therapy on biomarker levels. Females, patients with predominantly negative symptoms, and those taking atypical antipsychotics had a more pro-inflammatory phenotype. Using cluster analysis, we classified participants into "high" and "low inflammation" subgroups. However, no differences were found in the clinical data of patients in these subgroups. Nevertheless, more patients (17% to 25.5%) than healthy donors (8.6% to 14.3%) had evidence of a pro-inflammatory condition depending on the clustering approach used. Such patients may benefit from personalized anti-inflammatory therapy.

Characteristic Gut Bacteria in High Barley Consuming Japanese Individuals without Hypertension

BACKGROUND

Barley, a grain rich in soluble dietary fiber β-glucan, is expected to lower blood pressure. Conversely, individual differences in its effects on the host might be an issue, and gut bacterial composition may be a determinant.

METHODS

Using data from a cross-sectional study, we examined whether the gut bacterial composition could explain the classification of a population with hypertension risks despite their high barley consumption. Participants with high barley intake and no occurrence of hypertension were defined as "responders" (n = 26), whereas participants with high barley intake and hypertension risks were defined as "non-responders" (n = 39).

RESULTS

16S rRNA gene sequencing revealed that feces from the responders presented higher levels of Faecalibacterium, Ruminococcaceae UCG-013, Lachnospira, and Subdoligranulum and lower levels of Lachnoclostridium and Prevotella 9 than that from non-responders. We further created a machine-learning responder classification model using random forest based on gut bacteria with an area under the curve value of 0.75 for estimating the effect of barley on the development of hypertension.

CONCLUSIONS

Our findings establish a link between the gut bacteria characteristics and the predicted control of blood pressure provided by barley intake, thereby providing a framework for the future development of personalized dietary strategies.