Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance.

Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease. Using leave-one-out cross-validation (LOOCV), we found that an LSTM-based model combining TSR and serial PSA density (AUC 0.86 [95% CI: 0.78-0.94]) significantly outperformed a model combining conventional delta-radiomics and delta-PSA density (0.75 [0.64-0.87]; p = 0.048) and achieved comparable performance to expert-performed serial MRI analysis using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) scoring system (0.84 [0.76-0.93]; p = 0.710). The proposed TSR framework, therefore, offers a feasible quantitative tool for standardising serial MRI assessment in PCa AS. It also presents a novel methodological approach to serial image analysis that can be used to support clinical decision-making in multiple scenarios, from continuous disease monitoring to treatment response evaluation. KEY POINTS: •LSTM RNN can be used to predict the outcome of PCa AS using time series changes in tumour-derived radiomic features and PSA density. •Using all available TSR features and serial PSA density yields a significantly better predictive performance compared to using just two time points within the delta-radiomics framework. •The concept of TSR can be applied to other clinical scenarios involving serial imaging, setting out a new field in AI-driven radiology research.

Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance.

OBJECTIVES: To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). METHODS: The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test. RESULTS: The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77). CONCLUSIONS: PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. KEY POINTS: • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.

Feasibility of in vitro maturation of oocytes collected from patients with malignant ovarian tumors undergoing fertility preservation.

OBJECTIVE: In vitro maturation of oocytes collected from oophorectomy samples might be a promising approach in the field of oncofertility. In this study, we evaluate the feasibility of in vitro maturation of oocytes collected from oophorectomy samples in patients with ovarian tumors. METHODS: This prospective observational study included 27 patients with malignant ovarian tumors. Patients underwent oophorectomy and ovarian tissue was examined for the presence of immature cumulus-oocyte complexes. These were matured in vitro for 48 hours. Mature oocytes were vitrified or used for fertilization. Serum anti-müllerian hormone (AMH) levels were analyzed in 11 patients and cancer antigen 125 (CA125) levels in 16 patients. RESULTS: In this study, 99 cumulus-oocyte complexes were obtained from 17 patients (63%). The mean (SE) age of the patients was 33.47±1.86 years (range 16-44). A total of 14 patients had ovarian cancer (IA-IVB), one patient had ovarian cancer IC and endometrial cancer IA, one patient had endometrial cancer stage IA with metastasis into the ovary, and one patient had cervical cancer stage IIB with metastasis in the ovary. Oocytes were not obtained in 10 patients who had diminished ovarian reserve due to age (>38 years), chemotherapy, or previous surgical treatment. On average, 5.8 cumulus-oocyte complexes were obtained per patient. The maturation rate was 40.4% with an average of 2.8 metaphase II oocytes per patient. As a result of the study, 3 blastocysts in 3 patients and 22 oocytes in 9 patients were vitrified. CONCLUSIONS: In vitro maturation of oocytes collected from oophorectomy samples in patients with malignant ovarian tumors may result in oocyte and blastocyst vitrification. However, it should be offered to patients before surgery and chemotherapy. This method might be most beneficial in patients younger than 38 years, with AMH serum levels >1 ng/mL and without a large tumor burden.

Outcomes of ovarian stimulation in the follicular and luteal phases of the menstrual cycle in cancer patients.

OBJECTIVE: The purpose of the study was to evaluate the effectiveness of controlled ovarian stimulation in different phases of the menstrual cycle in cancer patients seeking for the preservation of reproductive material before gonadotoxic therapy. METHODS: A total of 140 patients with oncological diseases underwent ovarian stimulation in the standard protocol with GnRH antagonists in the follicular phase of the cycle (n = 68) and in the random-start protocol in the luteal phase of the menstrual cycle without prescribing GnRH antagonists (n = 72). RESULTS: All patients included in the study were comparable in age and AMH level. There were no differences in the mean number of oocytes retrieved in the follicular phase group, and luteal phase group. Similarly, no significant differences were observed in the number of M II oocytes. CONCLUSIONS: The results of the study demonstrate the equal effectiveness of stimulation protocols in different phases of the menstrual cycle, which allows us to develop a personalized approach to the implementation of reproductive function both in cancer patients and in the routine practice of ART.

Improved maturation competence of ovarian tissue oocytes using a biphasic in vitro maturation system for patients with gynecological malignancy: a study on sibling oocytes.

PURPOSE: To investigate the developmental competence of ovarian tissue oocytes from patients with gynecological tumors using a biphasic in vitro maturation system with capacitation (CAPA-IVM) in comparison with standard IVM. METHODS: This sibling pilot study included 210 oocytes in 10 patients with gynecological malignancies. After ovariectomies, ovaries were cut into even halves and immature cumulus-oocyte complexes (COCs) were retrieved from the ovarian tissue. COCs were separately cultured in either a biphasic CAPA-IVM system for 53 h or in standard IVM for 48 h. After IVM, all COCs were denuded and mature oocytes were either vitrified (N=5) or used for ICSI (N=5). Embryos were cultured for 5-6 days and obtained blastocysts were vitrified. RESULTS: Use of the CAPA-IVM system led to a higher meiotic maturation rate in ovarian tissue oocytes (OTO) compared to standard IVM (56 vs 35%, p=0.0045) and had a tendency to result in lower degeneration after IVM. Only the CAPA-IVM method supported blastocyst formation. CONCLUSIONS: The biphasic in vitro maturation system improved the competence of OTO in comparison to the standard IVM method. The study suggests that fertility preservation programs could become more efficient using IVM after capacitation culture.

Cryopreservation of euploid blastocysts obtained after fertilization of in vitro matured ovarian tissue oocytes: a case report.

With the increased rate of stable remission after gonadotoxic cancer treatment, new methods of fertility preservation are required in order to provide the best possible care for oncological patients. Here, we report an original case of euploid blastocyst cryopreservation after in vitro maturation of ovarian tissue oocytes (OTO IVM). Thirty-three oocytes were obtained from the ovarian tissue after ovariectomy in the breast cancer patient. Six out of 12 matured oocytes fertilized successfully and 3 blastocysts were formed. Genetic investigation for mutations associated with this type of malignancy found that the patient is not a carrier. Preimplantation genetic testing was performed only for aneuploidies and found all 3 blastocysts to be euploid and suitable for embryo transfer. Our study showed that the ovarian tissue oocytes matured in vitro have the potential for euploid blastocyst formation after ICSI which could be screened for aneuploidies and inherited mutations and then be vitrified in order to provide the best fertility preservation strategy for women with cancer.

The role of abnormal hypermethylation of the HOXA10 and HOXA11 promoters in implantation failures in IVF programs.

Objective: The objective of this study is to investigate the association between methylation levels of HOXA10 and HOXA11 promoters, clinical parameters, and implantation outcomes after in vitro fertilization-embryo transfer (IVT-ET) cycles in women with repeated implantation failures and tubal infertility. Methods: Endometrium samples were collected from 34 women during implantation window before IVF-ET cycle to assess methylation status of HOXA10 and HOXA11 promoters using bisulfite sequencing. All participants had a tubal factor of infertility and at least two implantation failures in the anamnesis. A logistic regression model was used to predict the implantation outcome depending on methylation status and clinical parameters. Results: The methylation of CpG-islands of HOXA10 and HOXA11 promoters was identified in 76.5 and 100% of participants, respectively. The median methylation levels did not differ significantly between the groups with different implantation outcomes, but a logistic regression model based on HOXA10 and HOXA11 methylation and clinical parameters allowed to classify the implantation outcomes with the total percentage of correct predictions of 85.19%. Conclusions: Abnormal methylation levels of the HOXA10 and HOXA11 promoters were found in the endometrium of women with tubal infertility and repeated implantation failures. The findings suggest that methylation status could be an important factor of implantation failure during IVF-ET cycles.

Prediction of spontaneous preterm birth in women with previous full dilatation cesarean delivery.

BACKGROUND: A previous term (≥37 weeks' gestation), full-dilatation cesarean delivery is associated with an increased risk for a subsequent spontaneous preterm birth. The mechanism is unknown. We hypothesized that the cesarean delivery scar characteristics and scar position relative to the internal cervical os may compromise cervical function, thereby leading to shortening of the cervical length and spontaneous preterm birth. OBJECTIVE: This study aimed to determine the relationship of cesarean delivery scar characteristics and position, assessed by transvaginal ultrasound, in pregnant women with previous full-dilatation cesarean delivery with the risk of shortening cervical length and spontaneous preterm birth. STUDY DESIGN: This was a single-center, prospective cohort study of singleton pregnant women (14 to 24 weeks' gestation) with a previous term full-dilatation cesarean delivery who attended a high-risk preterm birth surveillance clinic (2017-2021). Women underwent transvaginal ultrasound assessment of cervical length, cesarean delivery scar distance relative to the internal cervical os, and scar niche parameters using a reproducible transvaginal ultrasound technique. Spontaneous preterm birth prophylactic interventions (vaginal cervical cerclage or vaginal progesterone) were offered for short cervical length (≤25 mm) and to women with a history of spontaneous preterm birth or late miscarriage after full-dilatation cesarean delivery. The primary outcome was spontaneous preterm birth; secondary outcomes included short cervical length and a need for prophylactic interventions. A multivariable logistic regression analysis was used to develop multiparameter models that combined cesarean delivery scar parameters, cervical length, history of full-dilatation cesarean delivery, and maternal characteristics. The predictive performance of models was examined using the area under the receiver operating characteristics curve and the detection rate at various fixed false positive rates. The optimal cutoff for cesarean delivery scar distance to best predict a short cervical length and spontaneous preterm birth was analyzed. RESULTS: Cesarean delivery scars were visualized in 90.5% (220/243) of the included women. The spontaneous preterm birth rate was 4.1% (10/243), and 12.8% (31/243) of women developed a short cervical length. A history- (n=4) or ultrasound-indicated (n=19) cervical cerclage was performed in 23 of 243 (9.5%) women; among those, 2 (8.7%) spontaneously delivered prematurely. A multiparameter model based on absolute scar distance from the internal os best predicted spontaneous preterm birth (area under the receiver operating characteristics curve, 0.73; 95% confidence interval, 0.57-0.89; detection rate of 60% for a fixed 25% false positive rate). Models based on the relative anatomic position of the cesarean delivery scar to the internal os and the cesarean delivery scar position with niche parameters (length, depth, and width) best predicted the development of a short cervical length (area under the receiver operating characteristics curve, 0.79 [95% confidence interval, 0.71-0.87]; and 0.81 [95% confidence interval, 0.73-0.89], respectively; detection rate of 73% at a fixed 25% false positive rate). Spontaneous preterm birth was significantly more likely when the cesarean delivery scar was <5.0 mm above or below the internal os (adjusted odds ratio, 6.87; 95% confidence interval, 1.34-58; P =.035). CONCLUSION: In pregnancies following a full-dilatation cesarean delivery, cesarean delivery scar characteristics and distance from the internal os identified women who were at risk for spontaneous preterm birth and developing short cervical length. Overall, the spontaneous preterm birth rate was low, but it was significantly increased among women with a scar located <5.0 mm above or below the internal cervical os. Shortening of cervical length was strongly associated with a low scar position. Our novel findings indicate that a low cesarean delivery scar can compromise the functional integrity of the internal cervical os, leading to cervical shortening and/or spontaneous preterm birth. Assessment of the cesarean delivery scar characteristics and position seem to have use in preterm birth clinical surveillance among women with a previous, full-dilatation cesarean delivery and could better identify women who would benefit from prophylactic interventions.

Stem-Cell-Derived Extracellular Vesicles: Unlocking New Possibilities for Treating Diminished Ovarian Reserve and Premature Ovarian Insufficiency.

Despite advancements in assisted reproductive technology (ART), achieving successful pregnancy rates remains challenging. Diminished ovarian reserve and premature ovarian insufficiency hinder IVF success-about 20% of in vitro fertilization (IVF) patients face a poor prognosis due to a low response, leading to higher cancellations and reduced birth rates. In an attempt to address the issue of premature ovarian insufficiency (POI), we conducted systematic PubMed and Web of Science research, using keywords "stem cells", "extracellular vesicles", "premature ovarian insufficiency", "diminished ovarian reserve" and "exosomes". Amid the complex ovarian dynamics and challenges like POI, stem cell therapy and particularly the use of extracellular vesicles (EVs), a great potential is shown. EVs trigger paracrine mechanisms via microRNAs and bioactive molecules, suppressing apoptosis, stimulating angiogenesis and activating latent regenerative potential. Key microRNAs influence estrogen secretion, proliferation and apoptosis resistance. Extracellular vesicles present a lot of possibilities for treating infertility, and understanding their molecular mechanisms is crucial for maximizing EVs' therapeutic potential in addressing ovarian disorders and promoting reproductive health.

Integrated Placental Modelling of Histology with Gene Expression to Identify Functional Impact on Fetal Growth.

Fetal growth restriction (FGR) is a leading cause of perinatal morbidity and mortality. Altered placental formation and functional capacity are major contributors to FGR pathogenesis. Relating placental structure to function across the placenta in healthy and FGR pregnancies remains largely unexplored but could improve understanding of placental diseases. We investigated integration of these parameters spatially in the term human placenta using predictive modelling. Systematic sampling was able to overcome heterogeneity in placental morphological and molecular features. Defects in villous development, elevated fibrosis, and reduced expression of growth and functional marker genes (IGF2, VEGA, SLC38A1, and SLC2A3) were seen in age-matched term FGR versus healthy control placentas. Characteristic histopathological changes with specific accompanying molecular signatures could be integrated through computational modelling to predict if the placenta came from a healthy or FGR pregnancy. Our findings yield new insights into the spatial relationship between placental structure and function and the etiology of FGR.

Serum biomarker-based early detection of pancreatic ductal adenocarcinomas with ensemble learning.

BACKGROUND: Earlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough. METHODS: The primary objective of the work presented here is to use a dataset that is prospectively collected, to quantify a set of cancer-associated proteins and construct multi-marker models with the capacity to predict PDAC years before diagnosis. The data used is part of a nested case-control study within the UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 218 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 249 matched non-cancer controls. We develop a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets. RESULTS: Here we show that with ensemble learning we can predict PDAC status with an AUC of 0.91 (95% CI 0.75-1.0), sensitivity of 92% (95% CI 0.54-1.0) at 90% specificity, up to 1 year prior to diagnosis, and at an AUC of 0.85 (95% CI 0.74-0.93) up to 2 years prior to diagnosis (sensitivity of 61%, 95% CI 0.17-0.83, at 90% specificity). CONCLUSIONS: The ensemble modelling strategy explored here outperforms considerably biomarker combinations cited in the literature. Further developments in the selection of classifiers balancing performance and heterogeneity should further enhance the predictive capacity of the method.

Pancreatic cancers are most frequently detected at an advanced stage. This limits treatment options and contributes to the dismal survival rates currently recorded. The development of new tests that could improve detection of early-stage disease is fundamental to improve outcomes. Here, we use advanced data analysis techniques to devise an early detection test for pancreatic cancer. We use data on markers in the blood from people enrolled on a screening trial. Our test correctly identifies as positive for pancreatic cancer 91% of the time up to 1 year prior to diagnosis, and 78% of the time up to 2 years prior to diagnosis. These results surpass previously reported tests and should encourage further evaluation of the test in different populations, to see whether it should be adopted in the clinic.

The WID-BC-index identifies women with primary poor prognostic breast cancer based on DNA methylation in cervical samples.

Genetic and non-genetic factors contribute to breast cancer development. An epigenome-based signature capturing these components in easily accessible samples could identify women at risk. Here, we analyse the DNA methylome in 2,818 cervical, 357 and 227 matched buccal and blood samples respectively, and 42 breast tissue samples from women with and without breast cancer. Utilising cervical liquid-based cytology samples, we develop the DNA methylation-based Women's risk IDentification for Breast Cancer index (WID-BC-index) that identifies women with breast cancer with an AUROC (Area Under the Receiver Operator Characteristic) of 0.84 (95% CI: 0.80-0.88) and 0.81 (95% CI: 0.76-0.86) in internal and external validation sets, respectively. CpGs at progesterone receptor binding sites hypomethylated in normal breast tissue of women with breast cancer or in BRCA mutation carriers are also hypomethylated in cervical samples of women with poor prognostic breast cancer. Our data indicate that a systemic epigenetic programming defect is highly prevalent in women who develop breast cancer. Further studies validating the WID-BC-index may enable clinical implementation for monitoring breast cancer risk.

A proteomic survival predictor for COVID-19 patients in intensive care.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.

Recovery of ovarian function and pregnancy in a patient with AML after myeloablative busulphan-based conditioning regimen.

We report a rare case of ovarian function recovery and pregnancy after hormone-replacement therapy (HRT) in the acute myeloblastic leukemia (AML) patient in third complete remission received hematopoietic stem cell transplantation (HSCT) with busulphan-based conditioning regimen. Successful engraftment of the donor cells and full donor's chimerism was achieved without the signs of leukemia. One year after HSCT the patient received a course of HRT as a treatment of hypergonadotropic hypogonadism. After 12 months of HRT the recovery of ovarian function was confirmed. Eight years after the HSCT spontaneous pregnancy occurred; heartbeat of the fetus was registered on week 7. Three weeks later a nonsevere vaginal bleeding occurred and the ultrasound examination showed a nondeveloping pregnancy. Genetic examination of the abortion material showed a full triploid genotype (69 XXX). To our knowledge this is a first case of ovarian function restoration and spontaneous pregnancy in a AML patient after multiple courses of high-dose chemotherapy and busulphan-based myeloablative conditioning for HSCT.

Parenclitic and Synolytic Networks Revisited.

Parenclitic networks provide a powerful and relatively new way to coerce multidimensional data into a graph form, enabling the application of graph theory to evaluate features. Different algorithms have been published for constructing parenclitic networks, leading to the question-which algorithm should be chosen? Initially, it was suggested to calculate the weight of an edge between two nodes of the network as a deviation from a linear regression, calculated for a dependence of one of these features on the other. This method works well, but not when features do not have a linear relationship. To overcome this, it was suggested to calculate edge weights as the distance from the area of most probable values by using a kernel density estimation. In these two approaches only one class (typically controls or healthy population) is used to construct a model. To take account of a second class, we have introduced synolytic networks, using a boundary between two classes on the feature-feature plane to estimate the weight of the edge between these features. Common to all these approaches is that topological indices can be used to evaluate the structure represented by the graphs. To compare these network approaches alongside more traditional machine-learning algorithms, we performed a substantial analysis using both synthetic data with a priori known structure and publicly available datasets used for the benchmarking of ML-algorithms. Such a comparison has shown that the main advantage of parenclitic and synolytic networks is their resistance to over-fitting (occurring when the number of features is greater than the number of subjects) compared to other ML approaches. Secondly, the capability to visualise data in a structured form, even when this structure is not a priori available allows for visual inspection and the application of well-established graph theory to their interpretation/application, eliminating the "black-box" nature of other ML approaches.

Clinical Relevance of Secreted Small Noncoding RNAs in an Embryo Implantation Potential Prediction at Morula and Blastocyst Development Stages.

Despite the improvements in biotechnological approaches and the selection of controlled ovarian hyperstimulation protocols, the resulting pregnancy rate from in vitro fertilization (IVF) protocols still does not exceed 30-40%. In this connection, there is an acute question of the development of a non-invasive, sensitive, and specific method for assessing the implantation potential of an embryo. A total of 110 subfertile couples were included in the study to undergo the IVF/ICSI program. Obtained embryos for transfer into the uterine cavity of patient cohort 1 (n = 60) and cohort 2 (n = 50) were excellent/good-quality blastocysts, and small noncoding RNA (sncRNA) content in the corresponding spent culture medium samples at the morula stage (n = 43) or at the blastocyst stage (n = 31) was analyzed by deep sequencing followed by qRT-PCR in real time. Two logistic regression models were developed to predict the implantation potential of the embryo with 100% sensitivity and 100% specificity: model 1 at the morula stage, using various combinations of hsa_piR_022258, hsa-let-7i-5p, hsa_piR_000765, hsa_piR_015249, hsa_piR_019122, and hsa_piR_008112, and model 2 at the blastocyst stage, using various combinations of hsa_piR_020497, hsa_piR_008113, hsa-miR-381-3p, hsa_piR_022258, and hsa-let-7a-5p. Protein products of sncRNA potential target genes participate in the selective turnover of proteins through the ubiquitination system and in the organization of the various cell cytoskeleton and nucleoskeleton structures, regulating the activity of the Hippo signaling pathway, which determines the fate specification of the blastomers.

A time-resolved proteomic and prognostic map of COVID-19.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.