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This article, titled "Diagnosis of parametrial endometriosis with transvaginal ultrasonography," evaluates the use of ultrasound in diagnosing different types of parametrial endometriosis. The study analyzed 36 patients with endometriosis and compared the ultrasound diagnosis with surgical-pathological findings. The results showed that ultrasound can detect endometriosis in the parametria, with higher sensitivity in the posterior compartment and higher specificity in the anterior and lateral sites. The study concludes that ultrasound is a useful tool for preoperative assessment, but further research is needed. [Extracted from the article]

Alternative splicing is crucial for cancer progression and can be targeted pharmacologically, yet identifying driver exons genome-wide remains challenging. We propose identifying such exons by associating statistically gene-level cancer dependencies from knockdown viability screens with splicing profiles and gene expression. Our models predict the effects of splicing perturbations on cell proliferation from transcriptomic data, enabling in silico RNA screening and prioritizing targets for splicing-based therapies. We identified 1,073 exons impacting cell proliferation, many from genes not previously linked to cancer. Experimental validation confirms their influence on proliferation, especially in highly proliferative cancer cell lines. Integrating pharmacological screens with splicing dependencies highlights the potential driver exons affecting drug sensitivity. Our models also allow predicting treatment outcomes from tumor transcriptomes, suggesting applications in precision oncology. This study presents an approach to identifying cancer driver exon and their therapeutic potential, emphasizing alternative splicing as a cancer target., (© 2024. The Author(s).)

Background: Having a simple and fast dividing organism capable of producing and exposing at its surface or secreting functional complex biomolecules with disulphide bridges is of great interest. The mycoplasma bacterial genus offers a set of relevant properties that make it an interesting chassis for such purposes, the main one being the absence of a cell wall. However, due to their slow growth, they have rarely been considered as a potential platform in this respect. This notion may be challenged with the recent discovery of Mycoplasma feriruminatoris, a species with a dividing time close to that of common microbial workhorses. So far, no tools for heterologous protein expression nor secretion have been described for it., Results: The work presented here develops the fast-dividing M. feriruminatoris as a tool for secreting functional biomolecules of therapeutic interest that could be used for screening functional mutants as well as potentially for protein-protein interactions. Based on RNAseq, quantitative proteomics and promoter sequence comparison we have rationally designed optimal promoter sequences. Then, using in silico analysis, we have identified putative secretion signals that we validated using a luminescent reporter. The potential of the resulting secretion cassette has been shown with set of active clinically relevant proteins (interleukins and nanobodies)., Conclusions: We have engineered Mycoplasma feriruminatoris for producing and secreting functional proteins of medical interest., (© 2024. The Author(s).)

Alternative Splicing (AS) programs serve as instructive signals of cell type specificity, particularly within the brain, which comprises dozens of molecularly and functionally distinct cell types. Among them, retinal photoreceptors stand out due to their unique transcriptome, making them a particularly well-suited system for studying how AS shapes cell type-specific molecular functions. Here, we use the Splicing Regulatory State (SRS) as a novel framework to discuss the splicing factors governing the unique AS pattern of photoreceptors, and how this pattern may aid in the specification of their highly specialized sensory cilia. In addition, we discuss how other sensory cells with ciliated structures, for which data is much scarcer, also rely on specific SRSs to implement a proteome specialized in the detection of sensory stimuli. By reviewing the general rules of cell type- and tissue-specific AS programs, firstly in the brain and subsequently in specialized sensory neurons, we propose a novel paradigm on how SRSs are established and how they can diversify. Finally, we illustrate how SRSs shape the outcome of mutations in splicing factors to produce cell type-specific phenotypes that can lead to various human diseases., (© 2024. The Author(s).)

In recent years, essential oils (EOs) have received interest due to their antibacterial properties. Accordingly, the present study aimed to investigate the effectiveness of the EOs obtained from seven species of Salvia on three strains of Listeria monocytogenes (two serotyped wild strains and one ATCC strain), a bacterium able to contaminate food products and cause foodborne disease in humans. The Salvia species analysed in the present study were cultivated at the Botanic Garden and Museum of the University of Pisa, and their air-dried aerial parts were subjected to hydrodistillation using a Clevenger apparatus. The obtained EOs were analysed via gas chromatography coupled with mass spectrometry for the evaluation of their chemical composition, and they were tested for their inhibitory and bactericidal activities by means of MIC and MBC. The tested Eos showed promising results, and the best outcomes were reached by S. chamaedryoides EO, showing an MIC of 1:256 and an MBC of 1:64. The predominant compounds of this EO were the sesquiterpenes caryophyllene oxide and β-caryophyllene, together with the monoterpenes bornyl acetate and borneol. These results suggest that these EOs may possibly be used in the food industry as preservatives of natural origins.

Perineuronal nets (PNNs) surround specific neurons in the brain and are involved in various forms of plasticity and clinical conditions. However, our understanding of the PNN role in these phenomena is limited by the lack of highly quantitative maps of PNN distribution and association with specific cell types. Here, we present a comprehensive atlas of Wisteria floribunda agglutinin (WFA)-positive PNNs and colocalization with parvalbumin (PV) cells for over 600 regions of the adult mouse brain. Data analysis shows that PV expression is a good predictor of PNN aggregation. In the cortex, PNNs are dramatically enriched in layer 4 of all primary sensory areas in correlation with thalamocortical input density, and their distribution mirrors intracortical connectivity patterns. Gene expression analysis identifies many PNN-correlated genes. Strikingly, PNN-anticorrelated transcripts are enriched in synaptic plasticity genes, generalizing PNNs' role as circuit stability factors., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Inherited retinal diseases (IRDs) are a group of diseases whose common landmark is progressive photoreceptor loss. The development of gene-specific therapies for IRDs is hampered by their wide genetic heterogeneity. Mitochondrial dysfunction is proving to constitute one of the key pathogenic events in IRDs; hence, approaches that enhance mitochondrial activities have a promising therapeutic potential for these conditions. We previously reported that miR-181a/b downregulation boosts mitochondrial turnover in models of primary retinal mitochondrial diseases. Here, we show that miR-181a/b silencing has a beneficial effect also in IRDs. In particular, the injection in the subretinal space of an adeno-associated viral vector (AAV) that harbors a miR-181a/b inhibitor (sponge) sequence (AAV2/8-GFP-Sponge-miR-181a/b) improves retinal morphology and visual function both in models of autosomal dominant (RHO-P347S) and of autosomal recessive (rd10) retinitis pigmentosa. Moreover, we demonstrate that miR-181a/b downregulation modulates the level of the mitochondrial fission-related protein Drp1 and rescues the mitochondrial fragmentation in RHO-P347S photoreceptors. Overall, these data support the potential use of miR-181a/b downregulation as an innovative mutation-independent therapeutic strategy for IRDs, which can be effective both to delay disease progression and to aid gene-specific therapeutic approaches., (©2022 The Authors. Published under the terms of the CC BY 4.0 license.)

The automatic detection of violent actions in public places through video analysis is difficult because the employed Artificial Intelligence-based techniques often suffer from generalization problems. Indeed, these algorithms hinge on large quantities of annotated data and usually experience a drastic drop in performance when used in scenarios never seen during the supervised learning phase. In this paper, we introduce and publicly release the Bus Violence benchmark, the first large-scale collection of video clips for violence detection on public transport, where some actors simulated violent actions inside a moving bus in changing conditions, such as the background or light. Moreover, we conduct a performance analysis of several state-of-the-art video violence detectors pre-trained with general violence detection databases on this newly established use case. The achieved moderate performances reveal the difficulties in generalizing from these popular methods, indicating the need to have this new collection of labeled data, beneficial for specializing them in this new scenario.

Background: Immune-checkpoint inhibitors (ICIs) have increased and improved the treatment options for patients with non-oncogene-addicted advanced stage non-small cell lung cancer (NSCLC). However, the role of ICIs in oncogene-addicted advanced stage NSCLC patients is still debated. In this study, in an attempt to fill in the informational gap on the effect of ICIs on other driver mutations, we set out to provide a molecular landscape of clinically relevant oncogenic drivers in programmed death-ligand 1 (PD-L1) positive NSCLC patients., Methods: We retrospectively reviewed data on 167 advanced stage NSCLC PD-L1 positive patients (≥1%) who were referred to our clinic for molecular evaluation of five driver oncogenes, namely, EGFR , KRAS , BRAF , ALK and ROS1 ., Results: Interestingly, n = 93 (55.7%) patients showed at least one genomic alteration within the tested genes. Furthermore, analyzing a subset of patients with PD-L1 tumor proportion score (TPS) ≥ 50% and concomitant gene alterations ( n = 8), we found that n = 3 (37.5%) of these patients feature clinical benefit with ICIs administration, despite the presence of a concomitant KRAS gene alteration., Conclusions: In this study, we provide a molecular landscape of clinically relevant biomarkers in NSCLC PD-L1 positive patients, along with data evidencing the clinical benefit of ICIs in patient NSCLC PD-L1 positive alterations.

Exploiting well-labeled training sets has led deep learning models to astonishing results for counting biological structures in microscopy images. However, dealing with weak multi-rater annotations, i.e., when multiple human raters disagree due to non-trivial patterns, remains a relatively unexplored problem. More reliable labels can be obtained by aggregating and averaging the decisions given by several raters to the same data. Still, the scale of the counting task and the limited budget for labeling prohibit this. As a result, making the most with small quantities of multi-rater data is crucial. To this end, we propose a two-stage counting strategy in a weakly labeled data scenario. First, we detect and count the biological structures; then, in the second step, we refine the predictions, increasing the correlation between the scores assigned to the samples and the raters' agreement on the annotations. We assess our methodology on a novel dataset comprising fluorescence microscopy images of mice brains containing extracellular matrix aggregates named perineuronal nets. We demonstrate that we significantly enhance counting performance, improving confidence calibration by taking advantage of the redundant information characterizing the small sets of available multi-rater data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

In many working and recreational activities, there are scenarios where both individual and collective safety have to be constantly checked and properly signaled, as occurring in dangerous workplaces or during pandemic events like the recent COVID-19 disease. From wearing personal protective equipment to filling physical spaces with an adequate number of people, it is clear that a possibly automatic solution would help to check compliance with the established rules. Based on an off-the-shelf compact and low-cost hardware, we present a deployed real use-case embedded system capable of perceiving people's behavior and aggregations and supervising the appliance of a set of rules relying on a configurable plug-in framework. Working on indoor and outdoor environments, we show that our implementation of counting people aggregations, measuring their reciprocal physical distances, and checking the proper usage of protective equipment is an effective yet open framework for monitoring human activities in critical conditions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Published by Elsevier Ltd.)

Retinal photoreceptors have a distinct transcriptomic profile compared to other neuronal subtypes, likely reflecting their unique cellular morphology and function in the detection of light stimuli by way of the ciliary outer segment. We discovered a layer of this molecular specialization by revealing that the vertebrate retina expresses the largest number of tissue-enriched microexons of all tissue types. A subset of these microexons is included exclusively in photoreceptor transcripts, particularly in genes involved in cilia biogenesis and vesicle-mediated transport. This microexon program is regulated by Srrm3 , a paralog of the neural microexon regulator Srrm4 . Despite the fact that both proteins positively regulate retina microexons in vitro, only Srrm3 is highly expressed in mature photoreceptors. Its deletion in zebrafish results in widespread down-regulation of microexon inclusion from early developmental stages, followed by other transcriptomic alterations, severe photoreceptor defects, and blindness. These results shed light on the transcriptomic specialization and functionality of photoreceptors, uncovering unique cell type-specific roles for Srrm3 and microexons with implications for retinal diseases.

Competing Interests: Declaration of interests We declare no competing interests.

RNA splicing is widely dysregulated in cancer, frequently due to altered expression or activity of splicing factors (SFs). Microexons are extremely small exons (3-27 nucleotides long) that are highly evolutionarily conserved and play critical roles in promoting neuronal differentiation and development. Inclusion of microexons in mRNA transcripts is mediated by the SF Serine/Arginine Repetitive Matrix 4 (SRRM4), whose expression is largely restricted to neural tissues. However, microexons have been largely overlooked in prior analyses of splicing in cancer, as their small size necessitates specialized computational approaches for their detection. Here, we demonstrate that despite having low expression in normal nonneural tissues, SRRM4 is further silenced in tumors, resulting in the suppression of normal microexon inclusion. Remarkably, SRRM4 is the most consistently silenced SF across all tumor types analyzed, implying a general advantage of microexon down-regulation in cancer independent of its tissue of origin. We show that this silencing is favorable for tumor growth, as decreased SRRM4 expression in tumors is correlated with an increase in mitotic gene expression, and up-regulation of SRRM4 in cancer cell lines dose-dependently inhibits proliferation in vitro and in a mouse xenograft model. Further, this proliferation inhibition is accompanied by induction of neural-like expression and splicing patterns in cancer cells, suggesting that SRRM4 expression shifts the cell state away from proliferation and toward differentiation. We therefore conclude that SRRM4 acts as a proliferation brake, and tumors gain a selective advantage by cutting off this brake., Competing Interests: The authors have declared that no competing interests exist.

The COVID-19 pandemic has posed and is continuously posing enormous societal and health challenges worldwide. The research community has mobilized to develop novel projects to find a cure or a vaccine, as well as to contribute to mass testing, which has been a critical measure to contain the infection in several countries. Through this article, we share our experiences and learnings as a group of volunteers at the Centre for Genomic Regulation (CRG) in Barcelona, Spain. As members of the ORFEU project, an initiative by the Government of Catalonia to achieve mass testing of people at risk and contain the epidemic in Spain, we share our motivations, challenges and the key lessons learnt, which we feel will help better prepare the global society to address similar situations in the future., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Ghose R et al.)

Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there has been an increasing interest in convolutional neural network-based architectures to execute such a task. One of these supervised networks' critical goals is to generalize the knowledge learned during the training phase to new scenarios with different characteristics. A suitably labeled dataset is essential to achieve this purpose. The main problem is that manually annotating a dataset usually requires a lot of human effort, and it is costly. To this end, we introduce ViPeD (Virtual Pedestrian Dataset), a new synthetically generated set of images collected with the highly photo-realistic graphical engine of the video game GTA V (Grand Theft Auto V), where annotations are automatically acquired. However, when training solely on the synthetic dataset, the model experiences a Synthetic2Real domain shift leading to a performance drop when applied to real-world images. To mitigate this gap, we propose two different domain adaptation techniques suitable for the pedestrian detection task, but possibly applicable to general object detection. Experiments show that the network trained with ViPeD can generalize over unseen real-world scenarios better than the detector trained over real-world data, exploiting the variety of our synthetic dataset. Furthermore, we demonstrate that with our domain adaptation techniques, we can reduce the Synthetic2Real domain shift, making the two domains closer and obtaining a performance improvement when testing the network over the real-world images.