Co-Twin Prognosis After Single Intrauterine Fetal Death at a Tertiary Care Hospital in India: A Retrospective Observational Study.

INTRODUCTION: Twin pregnancy is associated with an increased risk of perinatal morbidity. Besides, if intrauterine death of a single twin occurs, it increases the morbidity of the surviving co-twin perinatally and postnatally. AIM:  The objective of this study was to determine the incidence of single intrauterine fetal death (SIUFD) in a twin pregnancy and fetal outcome defined in dimensions according to the complications in the surviving co-twin. MATERIAL AND METHODS: Data on twin pregnancies were collected retrospectively for a period of five years (from 2015 to 2019) from the labour room records of the Central Records Department (CRD) at the Postgraduate Institute of Medical Education and Research, Chandigarh, India. Cases with SIUFD were studied individually and neonatal follow-up was taken post delivery for up to three to eight years. Inclusion criteria were SIUFD in twin pregnancies after 14 weeks gestation, chorionicity pre-defined by early trimester ultrasonography. Exclusion criteria were higher-order pregnancy and monoamniotic twins. RESULTS: A total of 1246 (4.273%) twin deliveries were conducted in the study period. Of these, 107 (8.587%) pregnancies had SIUFD with co-twin surviving in utero. Among these, 77 (72%) were dichorionic diamniotic (DCDA) twin pregnancies and 30 (28%) were monochorionic diamniotic (MCDA) twin pregnancies. The incidence of SIUFD was 8.5%. Preterm birth was the most common complication observed in our study and was found in 53.5% and 58.3% of participants in DCDA and MCDA twins, respectively. Early neonatal death (within 24 hours of life) of the surviving twin was found in 29.2% monochorionic twins with SIUFD. SIUFD at < 28 weeks gestation led to a greater number of early neonatal deaths of surviving twins. The incidence of neurodevelopmental disorders (cerebral palsy, developmental delay, epilepsy) in our population after birth was 7.5% (n=93). CONCLUSION: Twin pregnancies with SIUFD have an increased incidence of preterm labour, increased neonatal death of the surviving twin, and neurodevelopmental disorders (cerebral palsy, developmental delay, epilepsy). Monochorionicity and SIUFD at <28 weeks gestation are associated with increased neonatal deaths in co-twin. The Incidence of neurodevelopmental disorders is not directly associated with chorionicity, but developmental delay is more profoundly seen in the monochorionic group.

Real-World-Time Data and RCT Synergy: Advancing Personalized Medicine and Sarcoma Care through Digital Innovation.

This manuscript examines the synergistic potential of prospective real-world/time data/evidence (RWTD/E) and randomized controlled trials (RCTs) to enrich healthcare research and operational insights, with a particular focus on its impact within the sarcoma field. Through exploring RWTD/E's capability to provide real-world/time, granular patient data, it offers an enriched perspective on healthcare outcomes and delivery, notably in the complex arena of sarcoma care. Highlighting the complementarity between RWTD/E's expansive real-world/time scope and the structured environment of RCTs, this paper showcases their combined strength, which can help to foster advancements in personalized medicine and population health management, exemplified through the lens of sarcoma treatment. The manuscript further outlines methodological innovations such as target trial emulation and their significance in enhancing the precision and applicability of RWTD/E, underscoring the transformative potential of these advancements in sarcoma care and beyond. By advocating for the strategic incorporation of prospective RWTD/E into healthcare frameworks, it aims to create an evidence-driven ecosystem that significantly improves patient outcomes and healthcare efficiency, with sarcoma care serving as a pivotal domain for these developments.

Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare.

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

HEP® (Homeostasis-Enrichment-Plasticity) Approach Changes Sensory-Motor Development Trajectory and Improves Parental Goals: A Single Subject Study of an Infant with Hemiparetic Cerebral Palsy and Twin Anemia Polycythemia Sequence (TAPS).

BACKGROUND: Early intervention (EI) for infants identified as being at high risk for cerebral palsy (CP), or who have been diagnosed with it, is critical for promotion of postnatal brain organization. The aim of this study was to explore the effectiveness of the Homeostasis-Enrichment-Plasticity (HEP) Approach, which is a contemporary EI model that applies the key principles of enriched environment paradigms and neuronal plasticity from experimental animal studies to ecological theories of human development on the motor development, sensory functions, and parental goals of an infant with twin anemia polycythemia sequence (TAPS) and CP. METHODS: An AB phase with follow-up single case study design which consisted of multiple baseline assessments with the Peabody Developmental Motor Scales-2 (PDMS-2) and the Test of Sensory Functions in Infants (TSFI) was used. Non-overlapping confidence intervals analysis was used for pre-post PDMS-2 scores. The measurement of progress toward goals and objectives was conducted using the Goal Attainment Scale (GAS). The HEP Approach intervention consisted of 12 one-hour sessions implemented over a period of 3 months, where a physical therapist provided weekly clinic-based parental coaching. RESULTS: Results found a stable baseline during Phase A and improvement in response to the HEP Approach intervention during Phase B in both the PDMS-2 and TSFI according to 2SD Band analysis. The confidence intervals for the PDMS-2 scores also indicated a significant improvement after HEP intervention. The scores for both the PDMS-2 and the TSFI were consistent or showed improvement throughout the Follow-Up phase. A GAS t-score of 77.14 indicated that the infant exceeded intervention goal expectations. CONCLUSIONS: Although our findings suggest that the HEP Approach intervention has promise in enhancing sensory functions, motor skill outcomes, and parental goals in an infant with TAPS and CP, further research is required to validate and apply these results more broadly.

Digital twins as a security risk?

Digital twins have become a popular and widely used tool for assessing risk and resilience, particularly as they have increased in the fidelity and accuracy of their representation of real-world systems. Although digital twins provide the ability to experiment on and assess risks to and from a system without damaging the real-world system, they pose potentially significant security risks. For example, if a digital twin of a power system has sufficient accuracy to allow loss of electrical power service due to a natural hazard to be estimated at the address level with a high degree of accuracy, what prevents someone wishing to lead to disruption at this same building from using the model to solve the inverse problem to determine which parts of the power system should be attacked to maximize the likelihood of loss of service to the target facility? This perspective article discusses the benefits and risks of digital twins and argues that more attention needs to be paid to the risks posed by digital twins.

Ingredient Functionality of Soy, Chickpea, and Pea Protein before and after Dry Heat Pretreatment and Low Moisture Extrusion.

This study investigates the impact of dry heat pretreatment on the functionality of soy, chickpea, and pea protein ingredients for use in texturized vegetable protein (TVP) production via low moisture extrusion. The protein powders were heat-treated at temperatures ranging from 80 °C to 160 °C to modulate the extent of protein denaturation and assess their effects on RVA pasting behavior, water absorption capacity (WAC), and color attributes. The results indicate that the pretreatment temperature significantly influenced the proteins' functional properties, with an optimal temperature of 120 °C enhancing pasting properties and maintaining WAC, while a higher pretreatment temperature of 160 °C led to diminished ingredient functionality. Different protein sources exhibited distinct responses to heat pretreatment. The subsequent extrusion processing revealed significant changes in extrudate density and color, with increased density and darkness observed at higher pretreatment temperatures. This research provides insights into the interplay between protein sources, pretreatment conditions, and extrusion outcomes, highlighting the importance of controlled protein denaturation for developing high-quality, plant-based meat analogues. The findings have broad implications for the optimization of meat analogue manufacturing, with the aim of enhancing the sensory experience and sustainability of plant-based foods.

Inline Hot Rolling of Al-5%Mg Strip Cast Using an Unequal Diameter Twin-Roll Caster.

One advantage of twin-roll casting for aluminum alloys is that hot rolling can be omitted, thus shortening the process. The effect of inline hot rolling on the anisotropy of the mechanical properties, especially the elongation, of the roll-cast strip has not been investigated. In a high-speed twin-roll caster, inline hot rolling forms the metal shape before the temperature of the cast strip decreases below the temperature needed for hot rolling. In this study, inline hot rolling of Al-5%Mg strips cast using an unequal diameter twin-roll caster was performed to validate the technique and evaluate its ability to reduce surface cracking and improve the elongation anisotropy. A rolling speed of 30 m/min was used, and the effects of temperature and thickness reduction during inline hot rolling on the surface and mechanical properties were investigated. Inline hot rolling was found to effectively reduce the formation of surface cracks and the anisotropy of the mechanical properties.

Maternal Circulating Vitamin D Level, Targeted Supplementation, and Perinatal Outcomes in Twin Pregnancy.

BACKGROUND: Vitamin D deficiency is associated with several obstetric complications in singleton pregnancy. The aim of this study was to assess whether vitamin D levels affect the outcomes of twin pregnancy and if targeted supplementation can improve perinatal outcomes. METHODS: The serum vitamin D levels of 143 women with twin pregnancies were measured during their first trimester. Those with insufficient (10-30 ng/mL; IL group) or severely deficient (<10 ng/mL, DL group) vitamin D levels were supplemented. In the third trimester, vitamin D levels were reassessed. Perinatal outcomes of the IL and DL groups were compared with those of patients with sufficient levels (>30 ng/mL, SL group) since the beginning of pregnancy. RESULTS: Women in the IL and DL groups had a higher incidence of hypertensive disorders of pregnancy (HDP) compared to the SL group (24.8% and 27.8% vs. 12.5%, p = 0.045): OR = 1.58 for the IL group and 1.94 for the DL group compared to the SL group. In patients whose vitamin D levels were restored after supplementation, HDP incidence was lower than in patients who remained in the IL or DL groups (23.4% vs. 27.3%) but higher than those who were always in the SL group (12.5%). CONCLUSIONS: Insufficient or severely deficient levels of vitamin D in the first trimester are associated with an increased risk of HDP in twin pregnancy. The beneficial effect of targeted vitamin D supplementation in reducing HDP seems limited.

Evaluation of Polyvinyl Alcohol as Binder during Continuous Twin Screw Wet Granulation.

Binder selection is a crucial step in continuous twin-screw wet granulation (TSWG), as the material experiences a much shorter residence time (2-40 s) in the granulator barrel compared to batch-wise granulation processes. Polyvinyl alcohol (PVA) 4-88 was identified as an effective binder during TSWG, but the potential of other PVA grades-differing in polymerization and hydrolysis degree-has not yet been studied. Therefore, the aim of the current study was to evaluate the potential of different PVA grades as a binder during TSWG. The breakage and drying behavior during the fluidized bed drying of drug-loaded granules containing the PVA grades was also studied. Three PVA grades (4-88, 18-88, and 40-88) were characterized and their attributes were compared to previously investigated binders by Vandevivere et al. through principal component analysis. Three binder clusters could be distinguished according to their attributes, whereby each cluster contained a PVA grade and a previously investigated binder. PVA 4-88 was the most effective binder of the PVA grades for both a good water-soluble and water-insoluble formulation. This could be attributed to its high total surface energy, low viscosity, good wettability of hydrophilic and hydrophobic surfaces, and good wettability by water of the binder. Compared to the previously investigated binders, all PVA grades were more effective in the water-insoluble formulation, as they yielded strong granules (friability below 30%) at lower L/S-ratios. This was linked to the high dispersive surface energy of the high-energy sites on the surface of PVA grades and their low surface tension. During fluidized bed drying, PVA grades proved suitable binders, as the acetaminophen (APAP) granules were dried within a short time due to the low L/S-ratio, at which high-quality granules could be produced. In addition, no attrition occurred, and strong tablets were obtained. Based on this study, PVA could be the preferred binder during twin screw granulation due to its high binder effectiveness at a low L/S-ratio, allowing efficient downstream processing. However, process robustness must be controlled by the included excipients, as PVA grades are operating in a narrow L/S-ratio range.

On the detection of population heterogeneity in causation between two variables: Finite mixture modeling of data collected from twin pairs.

Causal inference is inherently complex, often dependent on key assumptions that are sometimes overlooked. One such assumption is the potential for unidirectional or bidirectional causality, while another is population homogeneity, which suggests that the causal direction between two variables remains consistent across the study sample. Discerning these processes requires meticulous data collection through an appropriate research design and the use of suitable software to define and fit alternative models. In psychiatry, the co-occurrence of different disorders is common and can stem from various origins. A patient diagnosed with two disorders might have one recognized as primary and the other as secondary, suggesting the existence of two types of comorbidity within the population. For example, in some individuals, depression might lead to substance use, while in others, substance use could lead to depression. Identifying the primary disorder is crucial for developing effective treatment plans. This article explores the use of finite mixture models to depict within-sample heterogeneity. We begin with the Direction of Causation (DoC) model for twin data and extend it to a mixture distribution model. This extension allows for the calculation of the likelihood of each individual's data for the two alternate causal directions. Given twin data, there are four possible pairwise combinations of causal direction. Through simulations, we investigate the Direction of Causation Twin Mixture (mixCLPM) model's potential to detect and model heterogeneity due to varying causal directions.

Genetic and environmental determinants of bone quality: a cross-sectional analysis of the Hungarian Twin Registry.

There is abundant evidence that bone mineral content is highly heritable, while the heritability of bone quality (i.e. trabecular bone score [TBS] and quantitative ultrasound index [QUI]) is rarely investigated. We aimed to disentangle the role of genetic, shared and unique environmental factors on TBS and QUI among Hungarian twins. Our study includes 82 twin (48 monozygotic, 33 same-sex dizygotic) pairs from the Hungarian Twin Registry. TBS was determined by DXA, QUI by calcaneal bone ultrasound. To estimate the genetic and environmental effects, we utilized ACE-variance decomposition. For the unadjusted model of TBS, an AE model provided the best fit with > 80% additive genetic heritability. Adjustment for age, sex, BMI and smoking status improved model fit with 48.0% of total variance explained by independent variables. Furthermore, there was a strong dominant genetic effect (73.7%). In contrast, unadjusted and adjusted models for QUI showed an AE structure. Adjustments improved model fit and 25.7% of the total variance was explained by independent variables. Altogether 70-90% of the variance in QUI was related to additive genetic influences. We found a strong genetic heritability of bone quality in unadjusted models. Half of the variance of TBS was explained by age, sex and BMI. Furthermore, the adjusted model suggested that the genetic component of TBS could be dominant or an epistasis could be present. In contrast, independent variables explained only a quarter of the variance of QUI and the additive heritability explained more than half of all the variance.

First copy number variant in trans with single nucleotide variant in CCN6 causing progressive pseudorheumatoid dysplasia revealed by genome sequencing and deep phenotyping in monozygotic twins.

Biallelic pathogenic variants in CCN6 cause progressive pseudorheumatoid dysplasia (PPD), a rare skeletal dysplasia. The predominant features include noninflammatory progressive joint stiffness and enlargement, which are not unique to this condition. Nearly 100% of the reported variants are single nucleotide variants or small indels, and missing of a second variant has been reported. Genome sequencing (GS) covers various types of variants and deep phenotyping (DP) provides detailed and precise information facilitating genetic data interpretation. The combination of GS and DP improves diagnostic yield, especially in rare and undiagnosed diseases. We identified a novel compound heterozygote involving a disease-causing copy number variant (g.112057664_112064205del) in trans with a single nucleotide variant (c.624dup(p.Cys209MetfsTer21)) in CCN6 in a pair of monozygotic twins, through the methods of GS and DP. The twins had received three nondiagnostic results before. The g.112057664_112064205del variant was missed by all the tests, and the recorded phenotypes were inaccurate or even misleading. The twins were diagnosed with PPD, ending a 13-year diagnostic odyssey. There may be other patients with PPD experiencing underdiagnosis and misdiagnosis due to inadequate genetic testing or phenotyping methods. This case highlights the critical role of GS and DP in facilitating an accurate and timely diagnosis.

Genetics and sleep bruxism: a systematic review and meta-analysis of studies with twins.

OBJECTIVE: To systematically review the literature regarding the concordance of sleep bruxism (SB) between monozygotic (MZ) and dizygotic (DZ) twins. METHODS: The registration for this systematic review was accomplished in the International Prospective Register of Systematic Reviews (PROSPERO, No. CRD42021251751). As of July 2022, four databases were searched, including PubMed, Scopus, Embase, and Web of Science, as well as the grey literature in Google Scholar and OpenGrey. Observational studies evaluating SB in MZ and DZ twins of any age and sex were included. For the evaluation of the risk of bias, the Joanna Briggs checklist was utilized. The certainty of evidence was assessed via the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. Pooled and subgroup meta-analyses were performed to estimate concordance of SB ​​between twins (p < 0.05). RESULTS: In total, 3,155 records were identified. In the qualitative analysis, eleven studies were included; of these, seven were included in the meta-analysis. The majority of the articles exhibited a low risk of bias (63.6%). Greater SB concordance was observed between MZ twins than between DZ twins in the analysis of general concordance (OR = 1.47; 95% CI = 1.07-2.02) and also positive concordance (OR = 1.53; 95% CI = 1.29-1.81). Within the subgroup analyses, the significance of the findings remained only for the reported/self-reported SB regarding general concordance (OR = 1.44; 95% CI = 1.07-1.95) and positive concordance (OR = 1.55; 95% CI = 1.28-1.88). Low certainty of the evidence was observed for the general concordance analysis, while moderate certainty was observed for the positive concordance. CONCLUSION: There was a higher concordance of SB in MZ twins compared to DZ twins, indicating a possible genetic influence on the condition's occurrence.

Quantitative and calculated estimated blood loss in cesarean deliveries for twin and singleton pregnancies: a retrospective analysis.

PURPOSE: This study retrospectively assessed blood loss during cesarean deliveries for twin and singleton pregnancies using two distinct methods, quantitative estimation measured during cesarean sections and hematocrit-based calculated estimation. METHODS: We included scheduled cesarean deliveries for twin or singleton pregnancies at ≥ 34 weeks of gestation. Quantitative blood loss was recorded based on the blood volume in the graduated collector bottle and by weighing the blood-soaked textiles during cesarean sections. The blood loss was calculated using the change in hematocrit levels before and after the cesarean delivery. RESULTS: We evaluated 403 cases including 44 twins and 359 singletons. Quantitative blood loss during cesarean section was significantly higher in twin pregnancies than that in singleton pregnancies (1117 [440] vs 698 [378] mL; p < 0.001). However, no significant differences were observed in the calculated blood loss between the two groups on the day after delivery (487 mL [692 mL] vs 507 mL [522 mL]; p = 0.861). On post-delivery days 4-5, twin pregnancies were associated with a significantly higher calculated blood loss than singleton pregnancies (725 [868] mL vs 444 [565] mL, p = 0.041). Although a significant moderate correlation between quantitative and calculated blood loss was observed in singleton pregnancies (r = 0.473, p < 0.001), no significant correlation was observed between twin pregnancies (r = 0.053, p = 0.735). CONCLUSION: Quantitative blood loss measurements during cesarean section may be clinically insufficient in twin pregnancies. Incorporating blood tests and continuous assessments are warranted for enhanced blood loss evaluation, especially in twin pregnancies, owing to the risk of persistent bleeding.

Optimization of cooling rate of Q-P treated 42SiCr steel using AI digital twinning.

In the continuously advancing field of mechanical engineering, digitalization is bringing a major transformation, specifically with the concept of digital twins. Digital twins are dynamic digital models of real-world systems and processes, crucial for Industry 4.0 and the emerging Industry 5.0, which are changing how humans and machines work together in manufacturing. This paper explores the combination of physics-based and data-driven modeling using advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. This approach provides a comprehensive understanding of mechanical systems, improving materials design and manufacturing processes. The focus is on the advanced 42SiCr alloy, where AI-driven digital twinning is used to optimize cooling rates during Quenching and Partitioning (Q-P) treatments. This leads to significant improvements in the mechanical properties of 42SiCr steel. Given its complex properties influenced by various factors, this alloy is perfect for digital twinning. The Q-P heat treatment process not only restores the material's deformability but also gives it advanced high-strength steel (AHSS) properties. The findings show how AI and ML can effectively guide the development of high-strength steels and enhance their treatment processes. Additionally, integrating digital twins with new technologies like the Metaverse offers exciting possibilities for simulated production, remote monitoring, and collaborative design. By establishing a clear workflow from physical to digital twins and presenting empirical results, this paper connects theoretical modeling with practical applications, paving the way for smarter manufacturing solutions in mechanical engineering. Furthermore, this paper analyzes how digital twins can be integrated into advanced technologies like the Metaverse, opening up new possibilities for simulated production, remote monitoring, design collaboration, training simulations, analytics, and complete supply chain visibility. This integration is a crucial step toward realizing the full potential of digitalization in mechanical engineering.

Early childhood development strategy for the world's children with disabilities.

Early childhood is foundational for optimal and inclusive lifelong learning, health and well-being. Young children with disabilities face substantial risks of sub-optimal early childhood development (ECD), requiring targeted support to ensure equitable access to lifelong learning opportunities, especially in low- and middle-income countries. Although the Sustainable Development Goals, 2015-2030 (SDGs) emphasise inclusive education for children under 5 years with disabilities, there is no global strategy for achieving this goal since the launch of the SDGs. This paper explores a global ECD framework for children with disabilities based on a review of national ECD programmes from different world regions and relevant global ECD reports published since 2015. Available evidence suggests that any ECD strategy for young children with disabilities should consists of a twin-track approach, strong legislative support, guidelines for early intervention, family involvement, designated coordinating agencies, performance indicators, workforce recruitment and training, as well as explicit funding mechanisms and monitoring systems. This approach reinforces parental rights and liberty to choose appropriate support pathway for their children. We conclude that without a global disability-focussed ECD strategy that incorporates these key features under a dedicated global leadership, the SDGs vision and commitment for the world's children with disabilities are unlikely to be realised.

Development and Integration of a Digital Twin Model for a Real Hydroelectric Power Plant.

In this study, a digital twin model of a hydroelectric power plant has been created. Models of the entire power plant have been created and malfunction situations of a sensor located after the inlet valve of the plant have been analyzed using a programmable logic controller (PLC). As a feature of the digital twin (DT), the error prediction and prevention function has been studied specifically for the pressure sensor. The accuracy and reliability of the data obtained from the sensor are compared with the data obtained from the DT model. The comparison results are evaluated and erroneous data are identified. In this way, it is determined whether the malfunction occurring in the system is a real malfunction or a malfunction caused by measurement or connection errors. In the case of sensor failure or measurement-related malfunction, this situation is determined through the digital twin-based control mechanism. In the case of actual failure, the system is stopped, but in the case of measurement or connection errors, since the data are calculated by the DT model, the value in the specified region is known and thus there is no need to stop the system. This prevents production loss in the hydroelectric power plant by ensuring the continuity of the system in case of errors.

Anomaly Detection and Remaining Useful Life Estimation for the Health and Usage Monitoring Systems 2023 Data Challenge.

Gear fault detection and remaining useful life estimation are important tasks for monitoring the health of rotating machinery. In this study, a new benchmark for endurance gear vibration signals is presented and made publicly available. The new dataset was used in the HUMS 2023 conference data challenge to test anomaly detection algorithms. A survey of the suggested techniques is provided, demonstrating that traditional signal processing techniques interestingly outperform deep learning algorithms in this case. Of the 11 participating groups, only those that used traditional approaches achieved good results on most of the channels. Additionally, we introduce a signal processing anomaly detection algorithm and meticulously compare it to a standard deep learning anomaly detection algorithm using data from the HUMS 2023 challenge and simulated signals. The signal processing algorithm surpasses the deep learning algorithm on all tested channels and also on simulated data where there is an abundance of training data. Finally, we present a new digital twin that enables the estimation of the remaining useful life of the tested gear from the HUMS 2023 challenge.

Improving Sarcoma Outcomes: Target Trial Emulation to Compare the Impact of Unplanned and Planned Resections on the Outcome.

This study follows the Target Trial Emulation (TTE) framework to assess the impact of unplanned resections (UEs) and planned resections (PEs) of sarcomas on local recurrence-free survival (LRFS), metastasis-free survival (MFS), cancer-specific survival (CSS), and overall survival (OS). Sarcomas, malignant tumors with mesenchymal differentiation, present a significant clinical challenge due to their rarity, complexity, and the frequent occurrence of UEs, which complicates effective management. Our analysis utilized real-world-time data from the Swiss Sarcoma Network, encompassing 429 patients, to compare the impact of UEs and PEs, adjusting for known prognostic factors through a multivariable Cox regression model and propensity score weighting. Our findings reveal a significantly higher risk of local recurrence for UEs and a short-term follow-up period that showed no marked differences in MFS, CSS, and OS between the UE and PE groups, underlining the importance of optimal initial surgical management. Furthermore, tumor grade was validated as a critical prognostic factor, influencing outcomes irrespective of surgical strategy. This study illuminates the need for improved referral systems to specialized sarcoma networks to prevent UEs and advocates for the integration of TTE in sarcoma research to enhance clinical guidelines and decision-making in sarcoma care. Future research should focus on the prospective validations of these findings and the exploration of integrated care models to reduce the incidence of UEs and improve patient outcomes.

From transcriptomics to digital twins of organ function.

Cell level functions underlie tissue and organ physiology. Gene expression patterns offer extensive views of the pathways and processes within and between cells. Single cell transcriptomics provides detailed information on gene expression within cells, cell types, subtypes and their relative proportions in organs. Functional pathways can be scalably connected to physiological functions at the cell and organ levels. Integrating experimentally obtained gene expression patterns with prior knowledge of pathway interactions enables identification of networks underlying whole cell functions such as growth, contractility, and secretion. These pathways can be computationally modeled using differential equations to simulate cell and organ physiological dynamics regulated by gene expression changes. Such computational systems can be thought of as parts of digital twins of organs. Digital twins, at the core, need computational models that represent in detail and simulate how dynamics of pathways and networks give rise to whole cell level physiological functions. Integration of transcriptomic responses and numerical simulations could simulate and predict whole cell functional outputs from transcriptomic data. We developed a computational pipeline that integrates gene expression timelines and systems of coupled differential equations to generate cell-type selective dynamical models. We tested our integrative algorithm on the eicosanoid biosynthesis network in macrophages. Converting transcriptomic changes to a dynamical model allowed us to predict dynamics of prostaglandin and thromboxane synthesis and secretion by macrophages that matched published lipidomics data obtained in the same experiments. Integration of cell-level system biology simulations with genomic and clinical data using a knowledge graph framework will allow us to create explicit predictive models that mechanistically link genomic determinants to organ function. Such integration requires a multi-domain ontological framework to connect genomic determinants to gene expression and cell pathways and functions to organ level phenotypes in healthy and diseased states. These integrated scalable models of tissues and organs as accurate digital twins predict health and disease states for precision medicine.