Exploring the impact of sequence context on errors in SNP genotype calling with whole genome sequencing data using AI-based autoencoder approach.

A critical step in the analysis of whole genome sequencing data is variant calling. Despite its importance, variant calling is prone to errors. Our study investigated the association between incorrect single nucleotide polymorphism (SNP) calls and variant quality metrics and nucleotide context. In our study, incorrect SNPs were defined in 20 Holstein-Friesian cows by comparing their SNPs genotypes identified by whole genome sequencing with the IlluminaNovaSeq6000 and the EuroGMD50K genotyping microarray. The dataset was divided into the correct SNP set (666 333 SNPs) and the incorrect SNP set (4 557 SNPs). The training dataset consisted of only the correct SNPs, while the test dataset contained a balanced mix of all the incorrectly and correctly called SNPs. An autoencoder was constructed to identify systematically incorrect SNPs that were marked as outliers by a one-class support vector machine and isolation forest algorithms. The results showed that 59.53% (±0.39%) of the incorrect SNPs had systematic patterns, with the remainder being random errors. The frequent occurrence of the CGC 3-mer was due to mislabelling a call for C. Incorrect T instead of A call was associated with the presence of T in the neighbouring downstream position. These errors may arise due to the fluorescence patterns of nucleotide labelling.

Co-Targeting of DTYMK and PARP1 as a Potential Therapeutic Approach in Uveal Melanoma.

Uveal melanoma (UM) is the most common primary intraocular tumor in adults, with no standardized treatment for advanced disease. Based on preliminary bioinformatical analyses DTYMK and PARP1 were selected as potential therapeutic targets. High levels of both proteins were detected in uveal melanoma cells and correlated with increased tumor growth and poor prognosis. In vitro tests on MP41 (BAP1 positive) and MP46 (BAP1 negative) cancer cell lines using inhibitors pamiparib (PARP1) and Ymu1 (DTYMK) demonstrated significant cytotoxic effects. Combined treatment had synergistic effects in MP41 and additive in MP46 cell lines, reducing cell proliferation and inhibiting the mTOR signaling pathway. Furthermore, the applied inhibitors in combination decreased cell motility and migration speed, especially for BAP1-negative cell lines. Our hypothesis of the double hit into tumoral DNA metabolism as a possible therapeutic option in uveal melanoma was confirmed since combined targeting of DTYMK and PARP1 affected all tested cytophysiological parameters with the highest efficiency. Our in vitro findings provide insights into novel therapeutic avenues for managing uveal melanoma, warranting further exploration in preclinical and clinical settings.

No advantage of antimicrobial prophylaxis in AML/MDS/CMML patients treated with azacitidine-a prospective multicenter study by the Polish Adult Leukemia Group.

Introduction: Infections represent one of the most frequent causes of death of higher-risk MDS patients, as reported previously also by our group. Azacitidine Infection Risk Model (AIR), based on red blood cell (RBC) transfusion dependency, neutropenia <0.8 × 109/L, platelet count <50 × 109/L, albumin <35g/L, and ECOG performance status ≥2 has been proposed based on the retrospective data to estimate the risk of infection in azacitidine treated patients. Methods: The prospective non-intervention study aimed to identify factors predisposing to infection, validate the AIR score, and assess the impact of antimicrobial prophylaxis on the outcome of azacitidine-treated MDS/AML and CMML patients. Results: We collected data on 307 patients, 57.6 % males, treated with azacitidine: AML (37.8%), MDS (55.0%), and CMML (7.1%). The median age at azacitidine treatment commencement was 71 (range, 18-95) years. 200 (65%) patients were assigned to higher risk AIR group. Antibacterial, antifungal, and antiviral prophylaxis was used in 66.0%, 29.3%, and 25.7% of patients, respectively. In total, 169 infectious episodes (IE) were recorded in 118 (38.4%) patients within the first three azacitidine cycles. In a multivariate analysis ECOG status, RBC transfusion dependency, IPSS-R score, and CRP concentration were statistically significant for infection development (p < 0.05). The occurrence of infection within the first three azacitidine cycles was significantly higher in the higher risk AIR group - 47.0% than in lower risk 22.4% (odds ratio (OR) 3.06; 95% CI 1.82-5.30, p < 0.05). Administration of antimicrobial prophylaxis did not have a significant impact on all-infection occurrence in multivariate analysis: antibacterial prophylaxis (OR 0.93; 0.41-2.05, p = 0.87), antifungal OR 1.24 (0.54-2.85) (p = 0.59), antiviral OR 1.24 (0.53-2.82) (p = 0.60). Discussion: The AIR Model effectively discriminates infection-risk patients during azacitidine treatment. Antimicrobial prophylaxis does not decrease the infection rate.

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.

Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images. Evaluation of radiomics random survival forest models against clinical ones using Cox proportional hazard demonstrated comparable performance in predicting overall survival. However, radiomics outperformed clinical models in predicting progression-free survival. Explainable analysis highlighted the significance of non-tumoral VOI features, with their cumulative importance superior to features from the largest liver tumor. The proposed approach overcomes the limitations of manual VOI segmentation, requires no radiologist input and highlight the clinical relevance of features beyond tumor regions. Our findings suggest the potential of this radiomics models in predicting TACE outcomes, with possible implications for other clinical scenarios.

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 with 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 SARS­CoV­2 infection. Most risk assessment tools for AKI have been developed in the intensive care unit or in elderly populations. As the COVID­19 pandemic is transitioning into an endemic phase, there is an unmet need for prognostic scores tailored to the population of patients hospitalized for this disease. OBJECTIVES: We aimed to develop a robust predictive model for the occurrence of AKI in hospitalized patients with COVID­19. PATIENTS AND METHODS: Electronic medical records of all adult inpatients admitted between March 2020 and January 2022 were extracted from the database of a large, tertiary care center with a reference status in Lesser Poland. We screened 5806 patients with SARS­CoV­2 infection confirmed with a polymerase chain reaction test. After excluding individuals with lacking data on serum creatinine levels and those with a mild disease course (<7 days of inpatient care), a total of 4630 records were considered. Data were randomly split into training (n = 3462) and test (n = 1168) sets. A random forest model was tuned with feature engineering based on expert advice and metrics evaluated in nested cross­validation to reduce bias. RESULTS: Nested cross­validation yielded an area under the curve ranging between 0.793 and 0.807, and an average performance of 0.798. Model explanation techniques from a global perspective suggested that a need for respiratory support, chronic kidney disease, and procalcitonin concentration were among the most important variables in permutation tests. CONCLUSIONS: The CRACoV­AKI model enables AKI risk stratification among hospitalized patients with COVID­19. Machine learning-based tools may thus offer additional decision­making 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.

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.

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.

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.

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.