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Abstract Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic precision as compared to conventional two-dimensional (2D) mammography. In this study, we propose an advanced Computer-Aided Detection (CAD) system that harnesses the power of vision transformers to augment DBT’s diagnostic efficiency. This scheme uses a neural network to glean attributes from the 2D slices of DBT followed by post-processing that considers features from neighboring slices to categorize the entire 3D scan. By leveraging a transfer learning technique, we trained and validated our CAD framework on a unique dataset consisting of 3,831 DBT scans and subsequently tested it on 685 scans. Of the architectures tested, the Swin Transformer outperformed the ResNet101 and vanilla Vision Transformer. It achieved an impressive AUC score of 0.934 ± 0.026 at a resolution of 384 $$\times$$ × 384. Increasing the image resolution from 224 to 384 not only maintained vital image attributes but also led to a marked improvement in performance (p-value = 0.0003). The Mean Teacher algorithm, a semi-supervised method using both labeled and unlabeled DBT slices, showed no significant improvement over the supervised approach. Comprehensive analyses across different lesion types, sizes, and patient ages revealed consistent performance. The integration of attention mechanisms yielded a visual narrative of the model’s decision-making process that highlighted the prioritized regions during assessments. These findings should significantly propel the methodologies employed in DBT image analysis by setting a new benchmark for breast cancer diagnostic precision.

Abstract Therapies targeting the PD-1/PD-L1 pathway have transformed head and neck squamous cell carcinoma (HNSCC) treatment. However, predicting the response to anti-PD-1 therapy remains a clinical challenge. This study evaluated the functional binding of PD-1 ligands in 29 HNSCC patients and compared it to the standard PD-L1 Combined Positive Score (CPS). The assessment of PD-1 ligands’ functionality advances the current ability to predict the response of HNSCC patients to anti-PD-1 therapy.

This case report describes a 2-month-old girl with acute megakaryoblastic leukemia (AMKL) harboring the t(1;22)(p13;q13) translocation, resulting in the RBM15::MRTFA fusion gene. She presented with massive hepatosplenomegaly and liver fibrosis and achieved complete remission with chemotherapy; the liver fibrosis resolved within 2.5 months. After 12 years of follow-up, the patient remained in good health, without relapse. Reviewing the literature on eight additional similar cases of liver fibrosis, this subtype of AMKL predominantly affects female patients below 3 months of age, with a median onset at 6 weeks. High rates of severe complications were observed, with five of nine patients dying within 10 weeks of diagnosis. The authors hypothesized that the proliferation of abnormal megakaryoblasts within the liver leads to the release of profibrotic cytokines, such as TGF-β1, which induces liver fibrosis similar to that observed in transient abnormal myelopoiesis in Down syndrome. Careful monitoring of liver functions and reduced-intensity chemotherapy are recommended for this very young patient population. Nonetheless, long-term survival can be achieved with aggressive supportive care and treatment.

Abstract Background Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance. Methods The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast. Results The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners. Conclusions The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development – Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.

We assessed the anti-inflammatory activity of the TSPO ligand 2-Cl-MGV-1. Lipopolysaccharide (LPS) was used to induce inflammatory response in a murine RAW264.7 macrophage model (LPS: 100 ng/mL) and a mouse model (C57BL/6) of lung inflammation (LPS: 5 mg/kg). In the macrophage model, the presence of 2-Cl-MGV-1 (25 µM) caused the LPS-induced elevation in nitrite levels to decrease by 70% (p < 0.0001) and interleukin (IL)-6 by 50% (p < 0.05). In the mouse model, 2-Cl-MGV-1, administered 30 min before, or co-administered with, an LPS injection, significantly inhibited the elevation in serum IL-5 levels (both by 65%; p < 0.001 and p < 0.01, respectively). 2-Cl-MGV-1 administration to mice 30 min before LPS injection and 1 h thereafter significantly inhibited the elevation in IL-1β serum levels (both by 63%, p < 0.005). IL-6 elevation was inhibited by 73% (p < 0.005) when 2-Cl-MGV-1 was administered 30 min before LPS, by 60% (p < 0.05) when co-administered with LPS, and by 64% (p < 0.05) when administered 1 h after LPS. All cytokine assessments were conducted 6 h post LPS injection. Histological analyses showed decreased leukocyte adherence in the lung tissue of the ligand-treated mice. 2-Cl-MGV-1 administration 30 min prior to exposure to LPS inhibited inflammation-induced open field immobility. The beneficial effect of 2-Cl-MGV-1 suggests its potential as a therapeutic option for inflammatory diseases.

Introduction: Anal fissure (AF) is a common anorectal disease. Although several pharmacological treatments are available, many patients still require surgical interventions. In this study, we aimed to evaluate the efficacy of an ointment based on a multifunctional blend of herbal ingredients including hemp (ProctoFiz) for chronic AF. Methods: A single-arm, questionnaire-based prospective study was conducted in a large tertiary center to evaluate the outcomes of patients suffering from chronic AF treated with topical ProctoFiz. Results: Ninety-two patients were included in the study, 54 (58.7%) were females with a median age of 39 (range 17–78). 32 patients (34.7%) suffered from recurrent AF before enrolling in the study, and 5 patients (5.4%) underwent previous surgical interventions for AF. Three patients (3.2%) were lost to follow-up, leaving 89 patients for analysis. Eighty patients (89.9%) reported significant improvement of symptoms after 1 week using ProctoFiz, and 79 patients reported continued improvement after 1 month of treatment. The mean pain Visual Analog Score (VAS) declined by 6.6 points (8.9 vs. 2.3; 95% CI: 7.20 to −5.99, p < 0.0001) following 1 week of treatment, with continuous improvement to a mean of 0.64 after 1 month. Negative impact on quality of life significantly decreased from a mean of 8.8 to 0.38 following a month of treatment (p < 0.0001), with significant reduction in the number of patients suffering from bleeding following bowel movements (64.1–2.5%; p = 0.0001). Conclusion: Hemp-based topical treatment of AF is feasible and significantly improves AF-correlated symptoms.

Summary: Hippocampal subfield CA3 is thought to stably store memories in assemblies of recurrently connected cells functioning as a collective. However, the collective hippocampal coding properties that are unique to CA3 and how such properties facilitate the stability or precision of the neural code remain unclear. Here, we performed large-scale Ca2+ imaging in hippocampal CA1 and CA3 of freely behaving mice that repeatedly explored the same, initially novel environments over weeks. CA3 place cells have more precise and more stable tuning and show a higher statistical dependence with their peers compared with CA1 place cells, uncovering a cell assembly organization in CA3. Surprisingly, although tuning precision and long-term stability are correlated, cells with stronger peer dependence exhibit higher stability but not higher precision. Overall, our results expose the three-way relationship between tuning precision, long-term stability, and peer dependence, suggesting that a cell assembly organization underlies long-term storage of information in the hippocampus.

We hypothesized that targeted NGS sequencing might have an advantage over Sanger sequencing, especially in polymicrobial infections. The study included 55 specimens from 51 patients. We compared targeted NGS to Sanger sequencing in clinical samples submitted for Sanger sequencing. The overall concordance rate was 58% (32/55) for NGS vs. Sanger. NGS identified 9 polymicrobial and 2 monomicrobial infections among 19 Sanger-negative samples and 8 polymicrobial infections in 11 samples where a 16S gene was identified by gel electrophoresis, but could not be mapped to an identified pathogen by Sanger. We estimated that NGS could have contributed to patient management in 6/18 evaluated patients and thus has an advantage over Sanger sequencing in certain polymicrobial infections.

Cytokine storm refers to the dysregulated production of inflammatory mediators leading to hyperinflammation. They are often detrimental, and worsen the severity of COVID-19 and other infectious or inflammatory diseases. Cannabinoids are known to have anti-inflammatory effects but their possible therapeutic value on cytokine storms has not been fully elucidated. In vivo and ex vivo studies were carried out to investigate the effects of high-THC and high-CBD extracts on cytokine production in immune cells. Significant differences between the extracts were observed. Subsequent experiments focusing on a specific high CBD extract (CBD-X) showed significant reductions in pro-inflammatory cytokines in human-derived PBMCs, neutrophils and T cells. In vivo mouse studies, using a systemically inflamed mouse model, showed reductions in pro-inflammatory cytokines TNFα and IL-1β and a concurrent increase in the anti-inflammatory cytokine IL-10 in response to CBD-X extract treatment. Lung inflammation, as in severe COVID-19 disease, is characterized by increased T-cell homing to the lungs. Our investigation revealed that CBD-X extract impaired T-cell migration induced by the chemoattractant SDF1. In addition, the phosphorylation levels of T cell receptor (TCR) signaling proteins Lck and Zap70 were significantly reduced, demonstrating an inhibitory effect on the early events downstream to TCR activation. In a lung inflamed mouse model, we observed a reduction in leukocytes including neutrophil migration to the lungs and decreased levels of IL-1β, MCP-1, IL-6 and TNFα, in response to the administration of the high-CBD extract. The results presented in this work offer that certain high-CBD extract has a high potential in the management of pathological conditions, in which the secretion of cytokines is dysregulated, as it is in severe COVID-19 disease or other infectious or inflammatory diseases.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that involves difficulties in social communication. Previous research has demonstrated that these difficulties are apparent in the way ASD children speak, indicating that it may be possible to estimate ASD severity using quantitative features of speech. Here, we extracted a variety of prosodic, acoustic, and conversational features from speech recordings of Hebrew speaking children who completed an Autism Diagnostic Observation Schedule (ADOS) assessment. Sixty features were extracted from the recordings of 72 children and 21 of the features were significantly correlated with the children's ADOS scores. Positive correlations were found with pitch variability and Zero Crossing Rate (ZCR), while negative correlations were found with the speed and number of vocal responses to the clinician, and the overall number of vocalizations. Using these features, we built several Deep Neural Network (DNN) algorithms to estimate ADOS scores and compared their performance with Linear Regression and Support Vector Regression (SVR) models. We found that a Convolutional Neural Network (CNN) yielded the best results. This algorithm predicted ADOS scores with a mean RMSE of 4.65 and a mean correlation of 0.72 with the true ADOS scores when trained and tested on different sub-samples of the available data. Automated algorithms with the ability to predict ASD severity in a reliable and sensitive manner have the potential of revolutionizing early ASD identification, quantification of symptom severity, and assessment of treatment efficacy.

Applying information theoretic measures to neuronal activity data enables the quantification of neuronal encoding quality. However, when the sample size is limited, a naïve estimation of the information content typically contains a systematic overestimation (upward bias), which may lead to misinterpretation of coding characteristics. This bias is exacerbated in Ca2+ imaging because of the temporal sparsity of elevated Ca2+ signals. Here, we introduce methods to correct for the bias in the naïve estimation of information content from limited sample sizes and temporally sparse neuronal activity. We demonstrate the higher accuracy of our methods over previous ones, when applied to Ca2+ imaging data recorded from the mouse hippocampus and primary visual cortex, as well as to simulated data with matching tuning properties and firing statistics. Our bias-correction methods allowed an accurate estimation of the information place cells carry about the animal's position (spatial information) and uncovered the spatial resolution of hippocampal coding. Furthermore, using our methods, we found that cells with higher peak firing rates carry higher spatial information per spike and exposed differences between distinct hippocampal subfields in the long-term evolution of the spatial code. These results could be masked by the bias when applying the commonly used naïve calculation of information content. Thus, a bias-free estimation of information content can uncover otherwise overlooked properties of the neural code.

An automatic non-contact cough detector designed especially for night audio recordings that can distinguish coughs from snores and other sounds is presented. Two different classifiers were implemented and tested: a Gaussian Mixture Model (GMM) and a Deep Neural Network (DNN). The detected coughs were analyzed and compared in different sleep stages and in terms of severity of Obstructive Sleep Apnea (OSA), along with age, Body Mass Index (BMI), and gender. The database was composed of nocturnal audio signals from 89 subjects recorded during a polysomnography study. The DNN-based system outperformed the GMM-based system, at 99.8% accuracy, with a sensitivity and specificity of 86.1% and 99.9%, respectively (Positive Predictive Value (PPV) of 78.4%). Cough events were significantly more frequent during wakefulness than in the sleep stages (p < 0.0001) and were significantly less frequent during deep sleep than in other sleep stages (p < 0.0001). A positive correlation was found between BMI and the number of nocturnal coughs (R = 0.232, p < 0.05), and between the number of nocturnal coughs and OSA severity in men (R = 0.278, p < 0.05). This non-contact cough detection system may thus be implemented to track the progression of respiratory illnesses and test reactions to different medications even at night when a contact sensor is uncomfortable or infeasible.

The kidney glomerular filtration barrier (GFB) is enriched with heparan sulfate (HS) proteoglycans, which contribute to its permselectivity. The endoglycosidase heparanase cleaves HS and hence appears to be involved in the pathogenesis of kidney injury and glomerulonephritis. We have recently reported, nonetheless, that heparanase overexpression preserved glomerular structure and kidney function in an experimental model of Adriamycin-induced nephropathy. To elucidate mechanisms underlying heparanase function in podocytes—key GFB cells, we utilized a human podocyte cell line and transgenic mice overexpressing heparanase. Notably, podocytes overexpressing heparanase (H) demonstrated significantly higher survival rates and viability after exposure to Adriamycin or hydrogen peroxide, compared with mock-infected (V) podocytes. Immunofluorescence staining of kidney cryo-sections and cultured H and V podocytes as well as immunoblotting of proteins extracted from cultured cells, revealed that exposure to toxic injury resulted in a significant increase in autophagic flux in H podocytes, which was reversed by the heparanase inhibitor, Roneparstat (SST0001). Heparanase overexpression was also associated with substantial transcriptional upregulation of autophagy genes BCN1, ATG5, and ATG12, following Adriamycin treatment. Moreover, cleaved caspase-3 was attenuated in H podocytes exposed to Adriamycin, indicating lower apoptotic cell death in H vs. V podocytes. Collectively, these findings suggest that in podocytes, elevated levels of heparanase promote cytoprotection.

Neuronal tuning is typically measured in response to a priori defined behavioural variables of interest. Here, the authors use an unsupervised learning approach to recover neuronal tuning with respect to the recorded network activity and show that this can reveal the relevant behavioural variables.

Background: Heart failure is a major health problem and progress in this field relies on better understanding of the mechanisms and development of novel therapeutics using animal models. The rat may be preferable to the mouse as a cardiovascular disease model due to its closer physiology to humans and due to its large size that facilitates surgical and monitoring procedures. However, unlike the mouse, genetic manipulation of the rat genome is challenging. Methods: Here we developed a simple, refined, and robust cardiac-specific rat transgenic model based on an adeno-associated virus (AAV) 9 containing a cardiac troponin T promoter. This model uses a single intraperitoneal injection of AAV and does not require special expertise or equipment. Results: We characterize the AAV dose required to achieve a high cardiac specific level of expression of a transgene in the rat heart using a single intraperitoneal injection to neonates. We show that at this AAV dose GFP expression does not result in hypertrophy, a change in cardiac function or other evidence for toxicity. Conclusions: The model shown here allows easy and fast transgenic based disease modeling of cardiovascular disease in the rat heart, and can also potentially be expanded to deliver Cas9 and gRNAs or to deliver small hairpin (sh)RNAs to also achieve gene knockouts and knockdown in the rat heart.

Abstract Mitochondrial protein synthesis requires charging mt-tRNAs with their cognate amino acids by mitochondrial aminoacyl-tRNA synthetases, with the exception of glutaminyl mt-tRNA (mt-tRNAGln). mt-tRNAGln is indirectly charged by a transamidation reaction involving the GatCAB aminoacyl-tRNA amidotransferase complex. Defects involving the mitochondrial protein synthesis machinery cause a broad spectrum of disorders, with often fatal outcome. Here, we describe nine patients from five families with genetic defects in a GatCAB complex subunit, including QRSL1, GATB, and GATC, each showing a lethal metabolic cardiomyopathy syndrome. Functional studies reveal combined respiratory chain enzyme deficiencies and mitochondrial dysfunction. Aminoacylation of mt-tRNAGln and mitochondrial protein translation are deficient in patients’ fibroblasts cultured in the absence of glutamine but restore in high glutamine. Lentiviral rescue experiments and modeling in S. cerevisiae homologs confirm pathogenicity. Our study completes a decade of investigations on mitochondrial aminoacylation disorders, starting with DARS2 and ending with the GatCAB complex.