An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem.

The serious drawback underlying the biological annotation of whole-genome sequence data is the p >> n problem, which means that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). We propose a way to circumvent the problem by combining a LASSO logistic regression with deep learning to classify cows as susceptible or resistant to mastitis, based on single nucleotide polymorphism (SNP) genotypes. Among several architectures, the one with 204,642 SNPs was selected as the best. This architecture was composed of two layers with, respectively, 7 and 46 units per layer implementing respective drop-out rates of 0.210 and 0.358. The classification of the test data resulted in AUC = 0.750, accuracy = 0.650, sensitivity = 0.600, and specificity = 0.700. Significant SNPs were selected based on the SHapley Additive exPlanation (SHAP). As a final result, one GO term related to the biological process and thirteen GO terms related to molecular function were significantly enriched in the gene set that corresponded to the significant SNPs. Our findings revealed that the optimal approach can correctly predict susceptibility or resistance status for approximately 65% of cows. Genes marked by the most significant SNPs are related to the immune response and protein synthesis.

Predicting acute kidney injury onset using a random forest algorithm using electronic medical records of COVID-19 patients: the CRACoV-AKI model.

INTRODUCTION: Acute kidney injury (AKI) is a serious and common complication of SARS-CoV-2 infection. Most risk assessment tools for AKI have been developed in the intensive care unit or elderly population. As the COVID-19 pandemic is transitioning into an endemic state, there is an unmet need for prognostic scores tailored to this population. OBJECTIVES: Development of a robust predictive model for the occurrence of AKI in hospitalized patients with COVID-19. PATIENTS AND METHODS: Electronic medical records of all adult inpatients admitted between March 2020 and January 2022 were extracted from a large, tertiary care center with reference status in Lesser Poland. We screened 5806 patients with SARS-CoV-2 infection confirmed with polymerase chain reaction test. After excluding subjects with absent serum creatinine values or mild disease course (less than 7 days of inpatient care), 4630 patients were recruited. Data was randomly split into a training (N = 3462) and test (N = 1168) cohort. A random forest model was tuned with feature engineering based on expert advice and metrics evaluated in nested cross-validation to reduce bias. RESULTS: Nested cross-validation yielded an AUC (area under the curve) with a range of 0.793-0.807 and an average performance of 0.798. Model explanation techniques from a global perspective suggest respiratory support, chronic kidney disease and procalcitonin are among the most important variables in permutation tests. CONCLUSIONS: The CRACoV-AKI model enables AKI risk stratification among hospitalized patients with COVID-19. Machine learning-based tools may thus offer additional decision-making support for specialist providers.

Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data.

BACKGROUND: Nowadays, the chance of discovering the best antibody candidates for predicting clinical malaria has notably increased due to the availability of multi-sera data. The analysis of these data is typically divided into a feature selection phase followed by a predictive one where several models are constructed for predicting the outcome of interest. A key question in the analysis is to determine which antibodies  should be included in the predictive stage and whether they should be included in the original or a transformed scale (i.e. binary/dichotomized). METHODS: To answer this question, we developed three approaches for antibody selection in the context of predicting clinical malaria: (i) a basic and simple approach based on selecting antibodies via the nonparametric Mann-Whitney-Wilcoxon test; (ii) an optimal dychotomizationdichotomization approach where each antibody was selected according to the optimal cut-off via maximization of the chi-squared (χ2) statistic for two-way tables; (iii) a hybrid parametric/non-parametric approach that integrates Box-Cox transformation followed by a t-test, together with the use of finite mixture models and the Mann-Whitney-Wilcoxon test as a last resort. We illustrated the application of these three approaches with published serological data of 36 Plasmodium falciparum antigens for predicting clinical malaria in 121 Kenyan children. The predictive analysis was based on a Super Learner where predictions from multiple classifiers including the Random Forest were pooled together. RESULTS: Our results led to almost similar areas under the Receiver Operating Characteristic curves of 0.72 (95% CI = [0.62, 0.82]), 0.80 (95% CI = [0.71, 0.89]), 0.79 (95% CI = [0.7, 0.88]) for the simple, dichotomization and hybrid approaches, respectively. These approaches were based on 6, 20, and 16 antibodies, respectively. CONCLUSIONS: The three feature selection strategies provided a better predictive performance of the outcome when compared to the previous results relying on Random Forest including all the 36 antibodies (AUC = 0.68, 95% CI = [0.57;0.79]). Given the similar predictive performance, we recommended that the three strategies should be used in conjunction in the same data set and selected according to their complexity.

SATB2, CKAE1/AE3, and synaptophysin as a sensitive immunohistochemical panel for the detection of lymph node metastases of Merkel cell carcinoma.

Histopathological evaluation of lymph nodes in Merkel cell carcinoma has become crucial in progression estimation and treatment modification. This study was undertaken to determine the most sensitive immunohistochemical panel for detecting MCC nodal metastases. We included 56 patients with 102 metastatic MCC lymph nodes, which were tested with seven antibodies: cytokeratin (CKAE1/AE3), CK20, chromogranin A, synaptophysin, INSM1, SATB2, and neurofilament (NF). Tissue microarrays (TMA) composed of 2-mm tissue cores from each nodal metastasis were constructed. A semiquantitative 5-tier scoring system (0%, < 25%, 25-74%, 75-99%, 100% positive MCC cells with moderate to strong reactivity) was implemented. In the statistical assessment, we included Merkel cell polyomavirus (MCPyV) status and expression heterogeneity between lymph nodes from one patient. A cumulative percentage of moderate to strong expression ≥ 75% of tumoral cells was observed for single cell markers as follows: 91/102 (89.2%) SATB2, 85/102 (83%) CKAE1/AE3, 80/102 (78.4%) synaptophysin, 75/102 (75.5%) INSM1, 68/102 (66.7%) chromogranin A, 60/102 cases (58.8%) CK20, and 0/102 (0%) NF. Three markers presented a complete lack of immunoreactivity: 8/102 (7.8%) CK20, 7/102 (6.9%) chromogranin A, and 6/102 (5.9%) NF. All markers showed expression heterogeneity in lymph nodes from one patient; however, the most homogenous was INSM1. The probability of detecting nodal MCC metastases was the highest while using SATB2 as a first-line marker (89.2%) with subsequential adding CKAE1/AE3 (99%); these results were independent of MCPyV status. Synaptophysin showed a superior significance in confirming the neuroendocrine origin of metastatic cells. This comprehensive analysis allows us to recommend simultaneous evaluation of SATB2, CKAE1/AE3, and synaptophysin in the routine pathologic MCC lymph node protocol.

survex: an R package for explaining machine learning survival models.

SUMMARY: Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications. AVAILABILITY AND IMPLEMENTATION: survex is available under the GPL3 public license at https://github.com/modeloriented/survex and on CRAN with documentation available at https://modeloriented.github.io/survex.

miRNA Studies in Glaucoma: A Comprehensive Review of Current Knowledge and Future Perspectives.

Glaucoma, a neurodegenerative disorder that leads to irreversible blindness, remains a challenge because of its complex nature. MicroRNAs (miRNAs) are crucial regulators of gene expression and are associated with glaucoma and other diseases. We aimed to review and discuss the advantages and disadvantages of miRNA-focused molecular studies in glaucoma through discussing their potential as biomarkers for early detection and diagnosis; offering insights into molecular pathways and mechanisms; and discussing their potential utility with respect to personalized medicine, their therapeutic potential, and non-invasive monitoring. Limitations, such as variability, small sample sizes, sample specificity, and limited accessibility to ocular tissues, are also addressed, underscoring the need for robust protocols and collaboration. Reproducibility and validation are crucial to establish the credibility of miRNA research findings, and the integration of bioinformatics tools for miRNA database creation is a valuable component of a comprehensive approach to investigate miRNA aberrations in patients with glaucoma. Overall, miRNA research in glaucoma has provided significant insights into the molecular mechanisms of the disease, offering potential biomarkers, diagnostic tools, and therapeutic targets. However, addressing challenges such as variability and limited tissue accessibility is essential, and further investigations and validation will contribute to a deeper understanding of the functional significance of miRNAs in glaucoma.

miRNA-Seq Tissue Diagnostic Signature: A Novel Model for NSCLC Subtyping.

Non-small cell lung cancer (NSCLC) encompasses distinct histopathological subtypes, namely adenocarcinoma (AC) and squamous cell lung carcinoma (SCC), which require precise differentiation for effective treatment strategies. In this study, we present a novel molecular diagnostic model that integrates tissue-specific expression profiles of microRNAs (miRNAs) obtained through next-generation sequencing (NGS) to discriminate between AC and SCC subtypes of NSCLC. This approach offers a more comprehensive and precise molecular characterization compared to conventional methods such as histopathology or immunohistochemistry. Firstly, we identified 31 miRNAs with significant differential expression between AC and SCC cases. Subsequently, we constructed a 17-miRNA signature through rigorous multistep analyses, including LASSO/elastic net regression. The signature includes both upregulated miRNAs (hsa-miR-326, hsa-miR-450a-5p, hsa-miR-1287-5p, hsa-miR-556-5p, hsa-miR-542-3p, hsa-miR-30b-5p, hsa-miR-4728-3p, hsa-miR-450a-1-3p, hsa-miR-375, hsa-miR-147b, hsa-miR-7705, and hsa-miR-653-3p) and downregulated miRNAs (hsa-miR-944, hsa-miR-205-5p, hsa-miR-205-3p, hsa-miR-149-5p, and hsa-miR-6510-3p). To assess the discriminative capability of the 17-miRNA signature, we performed receiver operating characteristic (ROC) curve analysis, which demonstrated an impressive area under the curve (AUC) value of 0.994. Our findings highlight the exceptional diagnostic performance of the miRNA signature as a stratifying biomarker for distinguishing between AC and SCC subtypes in lung cancer. The developed molecular diagnostic model holds promise for providing a more accurate and comprehensive molecular characterization of NSCLC, thereby guiding personalized treatment decisions and improving clinical management and prognosis for patients.

Ki67 is a better marker than PRAME in risk stratification of BAP1-positive and BAP1-loss uveal melanomas.

BACKGROUND: Accurate risk stratification of uveal melanoma (UM) patients is important for determining the interval and frequency of surveillance. Loss of BAP1 expression has been shown to be strongly associated with UM-related death and metastasis. METHODS: In this study of 164 enucleated UMs, we assessed the prognostic role of preferentially expressed antigen in melanoma (PRAME) expression and Ki67 proliferation index measured by digital quantitation using QuPath programme in patients with BAP1-positive and BAP1-loss UMs. RESULTS: In univariate analyses with log-rank tests and Kaplan-Meier curves, PRAME further stratified only overall survival (OS) in BAP1-positive and BAP1-loss tumour groups. However, Ki67 further stratified both OS and disease-free survival (DFS) in BAP1-positive and BAP1-loss tumour groups. In multivariate analyses, Ki67 percentage and BAP1 were independent survival predictors for both OS and DFS, whereas PRAME was not a significant covariate. In model comparisons, combined Ki67 and BAP1 performed better than combined PRAME and BAP1 in risk-stratifying patients for both OS and DFS. Ki67 was better than PRAME in risk stratification of BAP1-positive UMs. Low Ki67 index correlated with significantly prolonged DFS in BAP1-loss UMs. CONCLUSION: A panel of Ki67 and BAP1 could be a helpful risk stratification strategy for UM.

Applied Molecular-Based Quality Control of Biobanked Samples for Multi-Omics Approach.

Biobanks are vital for high-throughput translational research, but the rapid development of novel molecular techniques, especially in omics assays, poses challenges to traditional practices and recommendations. In our study, we used biospecimens from oncological patients in Polish clinics and collaborated with the Indivumed Group. For serum/plasma samples, we monitored hemolysis, controlled RNA extraction, assessed cDNA library quality and quantity, and verified NGS raw data. Tissue samples underwent pathologic evaluation to confirm histology and determine tumor content. Molecular quality control measures included evaluating the RNA integrity number, assessing cDNA library quality and quantity, and analyzing NGS raw data. Our study yielded the creation of distinct workflows for conducting preanalytical quality control of serum/plasma and fresh-frozen tissue samples. These workflows offer customization options to suit the capabilities of different biobanking entities. In order to ensure the appropriateness of biospecimens for advanced research applications, we introduced molecular-based quality control methods that align with the demands of high-throughput assays. The novelty of proposed workflows, rooted in innovative molecular techniques, lies in the integration of these QC methods into a comprehensive schema specifically designed for high-throughput research applications.

Association analysis between symptomology and herpesvirus IgG antibody concentrations in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and multiple sclerosis.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and multiple sclerosis (MS) are two complex and multifactorial diseases whose patients experience persistent fatigue, cognitive impairment, among other shared symptoms. The onset of these diseases has also been linked to acute herpesvirus infections or their reactivations. In this work, we re-analyzed a previously-described dataset related to IgG antibody responses to 6 herpesviruses (CMV - cytomegalovirus; EBV - Epstein-Barr virus; HHV6 - human herpesvirus-6; HSV1 and HSV2 - herpes simplex virus-1 and -2, respectively; VZV - varicella-zoster virus) from the United Kingdom ME/CFS biobank. The primary goal was to report the underlying symptomology and its association with herpesvirus IgG antibodies using data from 4 disease-trigger-based subgroups of ME/CFS patients (n = 222) and patients with MS (n = 46). The secondary objective was to assess whether serological data could distinguish ME/CFS and its subgroup from MS using a SuperLearner (SL) algorithm. There was evidence for a significant negative association between temporary eye insight disturbance and CMV antibody concentrations and for a significant positive association between bladder problems and EBV antibody concentrations in the MS group. In the ME/CFS or its subgroups, the most significant antibody-symptom association was obtained for increasing HSV1 antibody concentration and brain fog, a finding in line with a negative impact of HSV1 exposure on cognitive outcomes in both healthy and disease conditions. There was also evidence for a higher number of significant antibody-symptom associations in the MS group than in the ME/CFS group. When we combined all the serological data in an SL algorithm, we could distinguish three ME/CFS subgroups (unknown disease trigger, non-infection trigger, and an infection disease trigger confirmed in the lab at the time of the event) from the MS group. However, we could not find the same for the remaining ME/CFS group (related to an unconfirmed infection disease). In conclusion, IgG antibody data explains more the symptomology of MS patients than the one of ME/CFS patients. Given the fluctuating nature of symptoms in ME/CFS patients, the clinical implication of these findings remains to be determined with a longitudinal study. This study is likely to ascertain the robustness of the associations during natural disease course.

Citrulline, Intestinal Fatty Acid-Binding Protein and the Acute Gastrointestinal Injury Score as Predictors of Gastrointestinal Failure in Patients with Sepsis and Septic Shock.

Gastrointestinal (GI) failure can be both a cause of sepsis and a consequence of the systemic pro-inflammatory response in sepsis. Changes in biomarkers of enterocyte damage, citrulline and I-FABP (intestinal fatty acid binding protein), may indicate altered intestinal permeability and damage. The study group consisted of patients with sepsis (N = 28) and septic shock (N = 30); the control group included patients without infection (N = 10). Blood samples were collected for citrulline and I-FABP and a 4-point AGI score (acute GI injury score) was calculated to monitor GI function on days 1, 3, 5, 7, and 10. Citrulline concentrations in the study group were lower than in the control. Lower values were also noted in septic patients with shock when compared to the non-shock group throughout the study period. I-FABP was higher in the septic shock group than in the sepsis group only on days 1 and 3. Citrulline was lower in patients with GI failure (AGI III) when compared to AGI I/II, reaching significance on days 7 (p = 0.034) and 10 (p = 0.015); moreover, a higher AGI score was associated with an increased 28 day mortality (p = 0.038). The results indicate that citrulline measurements, along with the AGI assessment, have clinical potential in monitoring GI function and integrity in sepsis.

Overexpression of c-MYC Promoter Binding Protein-1 Enhances Proliferation and Glucose Metabolism of Melanoma Cells Lines.

BACKGROUND/AIM: c-MYC promoter binding protein (MBP-1) is a product of alternatively translated mRNA encoding alpha-enolase (ENO1). In contrast to ENO1, MBP-1 possesses no enzymatic activity but acts as a transcriptional repressor of c-MYC. Ectopic over-expression of MBP-1 in tumor cells was shown to reduce cell proliferation and tumorigenicity, thus making it an attractive target for anticancer strategies. This study aimed to assess the effects of MBP-1 over-expression on human cutaneous melanoma cell lines. MATERIALS AND METHODS: We overexpressed the full-length MBP-1 or its C-terminal truncated variant (MBP-1ΔC), in two human melanoma cell lines (A375, WM9) and assessed their subcellular localization. qPCR was then used to quantitate c-MYC transcription. Further, 5-ethynyl-2'-deoxyuridine incorporation assay was used to measure cell proliferation and a lactate assay was performed to measure the glycolysis rate of cells in normoxia and hypoxia. Finally, an in vitro wound-healing assay was performed to evaluate cell migration. RESULTS: The overexpressed MBP-1 variants predominantly localized in the cytoplasm and barely decreased c-MYC expression. Unexpectedly, the proliferation rate of MBP-1- transduced cells increased in comparison to controls, as did the rate of glucose metabolism in hypoxia. Furthermore, over-expression of MBP-1, but not MBP-1ΔC, led to a substantial decrease in the cell migration capacity of metastatic WM9 cells but not A375 cells from the primary tumor lesion. CONCLUSION: Misslocalization of over-expressed MBP-1 in the cytoplasm of two melanoma cell lines resulted in an unexpected tumor promoting activity by increasing cell proliferation and glycolysis rates in hypoxia.

3D-printed custom ossicular prosthesis - methodology of design and LDV measurements in a cadaver study.

OBJECTIVE: In tympanoplasty, surgical reconstruction of the tympanic membrane and ossicular chain is well-established; however, its hearing results still require improvement. Custom 3D printing of individualized ossicular prostheses seems to be an attractive solution for optimal prosthesis adjustment and better hearing results. AIM: The aim was to design a custom ossicular prosthesis using a 3D printing method based on Cone-beam Computed Tomography (CBCT) scans and assess the acoustic conduction properties of such prosthesis. MATERIAL AND METHODS: A cadaver fresh frozen temporal bone was used. Based on CBCT images, a new incus prosthesis was designed and 3D printed. Next, canal wall-up tympanoplasty was performed. The intact ossicular chain and reconstructed 3D-printed prosthesis chain movements/vibrations were measured with Laser Doppler Vibrometer (LDV) system and analyzed in detail. RESULTS: The CBCT scans provided enough information about the anatomical structures. For frequencies 500 and 1000 Hz and 80 dB SPL sound intensity, collected velocities were higher for the intact ossicular chain than the 3D-printed ossicular prosthesis. The intensity thresholds for movement at 500 and 1000 Hz were lower in the intact ossicular chain than in the 3D-printed ossicular prosthesis. At 2000 Hz, there was the same intensity threshold value in the two measured circumstances. CONCLUSION: It is possible to design a custom individually fitted ossicular prosthesis using a 3D printing method based on CBCT scans. The acoustic conduction properties of such 3D-printed prosthesis showed differences in movability pattern between the intact and reconstructed ossicular chain. More data are needed to analyze the acoustic properties of such designed prostheses in detail. The results of our experiment showed the 3D-printed prosthesis presents the potential to be an interesting option for conductive hearing loss treatment caused by chronic otitis media and the ossicular chain defects.

The grammar of interactive explanatory model analysis.

The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One cannot sufficiently explain a black-box machine learning model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, leading to wrong or simplistic reasoning. This problem is known as the Rashomon effect and refers to diverse, even contradictory, interpretations of the same phenomenon. Surprisingly, most methods developed for explainable and responsible machine learning focus on a single-aspect of the model behavior. In contrast, we showcase the problem of explainability as an interactive and sequential analysis of a model. This paper proposes how different Explanatory Model Analysis (EMA) methods complement each other and discusses why it is essential to juxtapose them. The introduced process of Interactive EMA (IEMA) derives from the algorithmic side of explainable machine learning and aims to embrace ideas developed in cognitive sciences. We formalize the grammar of IEMA to describe human-model interaction. It is implemented in a widely used human-centered open-source software framework that adopts interactivity, customizability and automation as its main traits. We conduct a user study to evaluate the usefulness of IEMA, which indicates that an interactive sequential analysis of a model may increase the accuracy and confidence of human decision making.

SARS-CoV-2 Infection in Patients Treated with Azacitidine and Venetoclax for Acute Leukemia: A Report of a Case Series Treated in a Single Institution.

INTRODUCTION: Venetoclax combined with azacitidine (AZA-VEN) constitutes an option for the treatment of acute myeloid leukemia. There are, however, no data on the COVID-19 incidence and outcome in patients treated with AZA-VEN. METHODS: Patients with acute leukemia treated with AZA-VEN at a single institution were included in this prospective observational study. RESULTS: Thirteen patients were enrolled, 46% with treatment-naïve, and 56% with relapsed/refractory disease. Fifty-four percent of patients were males; the median age was 69 years. Six patients (46%) developed COVID-19 during the observation time. The median time to COVID-19 was 24 days from the initiation of AZA-VEN. The 2-month cumulative incidence of COVID-19 was 46.2%. Two patients (33%) succumbed to COVID-19. The 100-day COVID-19-free survival from AZA-VEN initiation was 61%. The median follow-up time was 4.3 months. DISCUSSION/CONCLUSION: COVID-19 constitutes a frequent complication of AZA-VEN treatment in the era of the COVID-19 pandemic, leading to death in a significant proportion of patients.

Multi-omics disease module detection with an explainable Greedy Decision Forest.

Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific disciplines, such as systems biology, domain knowledge is available in the form of graphs or networks, and its use can improve model performance. We need network-based algorithms that are versatile and applicable in many research areas. In this work, we demonstrate subnetwork detection based on multi-modal node features using a novel Greedy Decision Forest (GDF) with inherent interpretability. The latter will be a crucial factor to retain experts and gain their trust in such algorithms. To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well. Systems biology is a good example of a field in which statistical data-driven machine learning enables the analysis of large amounts of multi-modal biomedical data. This is important to reach the future goal of precision medicine, where the complexity of patients is modeled on a system level to best tailor medical decisions, health practices and therapies to the individual patient. Our proposed explainable approach can help to uncover disease-causing network modules from multi-omics data to better understand complex diseases such as cancer.

Quantitative analysis of RT-PCR test results for SARS-CoV-2 diagnostics across Poland during COVID-19 pandemic: Comparison between early stage and major pandemic waves in 2020 and 2021 with reference to SARS-CoV-2 variants.

PURPOSE: From April to September 2020, Poland was minimally affected by COVID-19 compared to other EU countries. We aimed to investigate the risks of false reverse transcription polymerase chain reaction (RT-PCR) results during the first wave (compared to later waves), that rises when cycle threshold (Ct) of positive result is close to limit of detection (LOD). MATERIALS/METHODS: We analyzed Ct values of SARS-CoV-2 positive RT-PCR results of 7726 patients in Poland from April-September 2020. SARS-CoV-2 positive RT-PCR results of 14,534 patients in the 2nd-3rd wave and 10,861 patients in the 4th-5th pandemic waves were used. Statistical analysis was based on one-way analysis of variance. To verify, 95% confidence intervals with Bonferroni correction were computed. Incidence of SARS-CoV-2 variants in Poland was analyzed using Whole Genome Sequencing from 923 (3.6%) patients. RESULTS: The mean Ct of RT-PCR positive test results analyzed ranged between 22.89 and 26.71 depending on the month of the results collection. The differences between months were significant (p â€‹< â€‹0.001). Differences in Ct were observed between age groups, with younger patients displaying higher Ct values, however, major trends over time were paralleled between age groups. CONCLUSIONS: The mean Ct of the tested RT-PCR positive test results was lower than 35 which is considered an upper borderline for reliable positive results of the assay. Therefore, most COVID-19 cases recorded in Poland from April to September 2020 were detected with minor risks of inaccuracy. Data from a single center exhibited greater consistency for both virus Ct level and SARS-CoV-2 virus variant identification.

Machine learning models demonstrate that clinicopathologic variables are comparable to gene expression prognostic signature in predicting survival in uveal melanoma.

PURPOSE: Since molecular assays are not accessible to all uveal melanoma patients, we aim to identify cost-effective prognostic tool in risk stratification using machine learning models based on routine histologic and clinical variables. EXPERIMENTAL DESIGN: We identified important prognostic parameters in a discovery cohort of 164 enucleated primary uveal melanomas from 164 patients without prior therapies. We then validated the prognostic prediction of top important parameters identified in the discovery cohort using 80 uveal melanomas from the Tumor Cancer Genome Atlas database with available gene expression prognostic signature (GEPS). The performance of three different survival analysis models (Cox proportional hazards (CPH), random survival forest (RSF), and survival gradient boosting (SGB)) was compared against GEPS using receiver operating curves (ROC). RESULTS: In all three selection methods, BAP1 status, nucleoli size, age, mitotic rate per 1 mm2, and ciliary body infiltration were identified as significant overall survival (OS) predictors; and BAP1 status, nucleoli size, largest basal tumor diameter, tumor-infiltrating lymphocyte density, and tumor-associated macrophage density were identified as significant progression-free survival (PFS) predictors. ROC plots for the median survival time point showed that significant parameters in SGB studied model can predict OS better than GEPS. For PFS, SGB model performed similarly to GEPS. The time-dependent area under the curve (AUC) showed SGB model performing better than GEPS in predicting OS and metastatic risk. CONCLUSIONS: Our study shows that routine histologic and clinical variables are adequate for patient risk stratification in comparison with not readily accessible GEPS.

Explainable Artificial Intelligence Helps in Understanding the Effect of Fibronectin on Survival of Sepsis.

Fibronectin (FN) plays an essential role in the host's response to infection. In previous studies, a significant decrease in the FN level was observed in sepsis; however, it has not been clearly elucidated how this parameter affects the patient's survival. To better understand the relationship between FN and survival, we utilized innovative approaches from the field of explainable machine learning, including local explanations (Break Down, Shapley Additive Values, Ceteris Paribus), to understand the contribution of FN to predicting individual patient survival. The methodology provides new opportunities to personalize informative predictions for patients. The results showed that the most important indicators for predicting survival in sepsis were INR, FN, age, and the APACHE II score. ROC curve analysis showed that the model's successful classification rate was 0.92, its sensitivity was 0.92, its positive predictive value was 0.76, and its accuracy was 0.79. To illustrate these possibilities, we have developed and shared a web-based risk calculator for exploring individual patient risk. The web application can be continuously updated with new data in order to further improve the model.