Search

Abstract Background The intricate three-dimensional anatomy of the inner ear presents significant challenges in diagnostic procedures and critical surgical interventions. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNN), have shown promise for segmenting specific structures in medical imaging. This study aimed to train and externally validate an open-source U-net DL general model for automated segmentation of the inner ear from computed tomography (CT) scans, using quantitative and qualitative assessments. Methods In this multicenter study, we retrospectively collected a dataset of 271 CT scans to train an open-source U-net CNN model. An external set of 70 CT scans was used to evaluate the performance of the trained model. The model’s efficacy was quantitatively assessed using the Dice similarity coefficient (DSC) and qualitatively assessed using a 4-level Likert score. For comparative analysis, manual segmentation served as the reference standard, with assessments made on both training and validation datasets, as well as stratified analysis of normal and pathological subgroups. Results The optimized model yielded a mean DSC of 0.83 and achieved a Likert score of 1 in 42% of the cases, in conjunction with a significantly reduced processing time. Nevertheless, 27% of the patients received an indeterminate Likert score of 4. Overall, the mean DSCs were notably higher in the validation dataset than in the training dataset. Conclusion This study supports the external validation of an open-source U-net model for the automated segmentation of the inner ear from CT scans. Relevance statement This study optimized and assessed an open-source general deep learning model for automated segmentation of the inner ear using temporal CT scans, offering perspectives for application in clinical routine. The model weights, study datasets, and baseline model are worldwide accessible. Key Points A general open-source deep learning model was trained for CT automated inner ear segmentation. The Dice similarity coefficient was 0.83 and a Likert score of 1 was attributed to 42% of automated segmentations. The influence of scanning protocols on the model performances remains to be assessed. Graphical Abstract

This study explores gas-solid contact systems, specifically fluidized beds, crucial in various industries. The focus is understanding their 3D model, hydrodynamics, and particle interactions among changing gas flow conditions. Utilizing a Computational Fluid Dynamics (CFD) model with the Eulerian method, the research navigates the complexities of multiphase and turbulent flows. The approach employs distinct equations for each phase, facilitating interaction within the computational domain. Turbulence effects are incorporated through the realizable k–ε model, known for its precision in representing turbulent behaviors in multiphase flow. The investigation investigates the complex interactions between phases using the continuity equation, emphasizing mass conservation with mass transfer terms capturing substance movement between phases. The Discrete Phase Model (DPM) is integral in understanding particle behavior, employing massless inert particles for targeted insights into system behavior and gas-solid interaction. Observations reveal that introduced particles have a limited impact on pressure dynamics, a crucial aspect of reactor design and optimization. The study explores the influence of varying gas and particle velocities on system pressure, turbulence kinetic energy, and particle distribution within the reactor. Results show minimal effects on pressure dynamics due to changes in particle velocity, with system pressure ranging from 0.33 Pa at the lowest air velocity of 0.5 m/s to 12.08 Pa at the highest air velocity of 3 m/s, establishing a nearly linear relationship between air velocity and pressure. The research extends to experimental validation, showing commendable agreement with computational findings. While empirical investigations are in preliminary stages, they promise further research optimization. This study provides a comprehensive understanding of gas-solid contact systems, emphasizing the importance of precise control over air and particle velocities for optimal system performance. The findings carry practical implications across diverse industrial applications and suggest avenues for continued research and development.

Electroencephalography source imaging (ESI) is an ill-posed inverse problem: an additional constraint is needed to find a unique solution. The choice of this constraint, or prior, remains a challenge for most ESI methods. This work explores the application of supervised learning methods for spatio-temporal ESI, where the relationship between measurements and sources is learned directly from the data. Three neural networks were trained on synthetic data and compared with non-learning based methods. Two distinct types of simulation, each based on different models of brain electrical activity, were employed to quantitatively assess the generalization capabilities of the neural networks and the impact of training data on their performances, using five complementary metrics. The results demonstrate that, with appropriately designed simulations, neural networks can be competitive with non-learning-based approaches, even when applied to previously unseen data.

Background Emicizumab, a bispecific factor VIII mimetic antibody, was approved in 2018 for bleeding prophylaxis in congenital hemophilia A with or without inhibitors. Since then, several case reports and case series have described the off-label use of emicizumab in acquired hemophilia A (AHA), and data from two clinical trials were recently published (AGEHA, GTH-AHA-EMI). Objectives To describe the reported data on the outcomes of emicizumab, highlighting its benefit/risk profile in treatment. Methods We conducted a literature search in PubMed, Scopus, Cochrane, and Google Scholar up to August 2024, including all scientific articles reporting clinical outcomes of emicizumab use in patients with AHA. Results Thirty-two studies were included in the final review, covering a total of 171 AHA patients. The majority started emicizumab for active bleeding management and prophylaxis with various regimens. Follow-up duration and remission criteria varied. Two clinical trials supported the use of emicizumab for bleeding prophylaxis with a new dosing regimen and completion criteria. Bleeding was well managed in all cases, with no major recurrent bleeds. Some adverse events were reported : 3 cases of deep venous thrombosis, 2 cases of stroke, and 2 cases of anti-emicizumab drug antibodies developing in patients with thromboembolic risk factors. Conclusions Based on published data, emicizumab appears to be effective in bleeding management and prophylaxis in AHA patients, with a favorable benefit/risk profile.

Management of reproduction that relies on naturally based solutions is extremely important to counter the negative perception around hormone-based interventions. In Mediterranean latitudes and wider regions of non-tropical drylands, sheep do not normally ovulate during spring but exposure to a ram can induce oestrus and ovulation. This study assesses the response of maiden Barbarine ewes in drylands to the ‘ram effect’ during springtime and estimates the genetic parameters of this response. The study uses a database documenting, for 24 consecutive years, the response of nulliparous 18-month-old ewes when mated after stimulation by the ‘ram effect.’ In addition to the oestrous and fertility database, a pedigree database was also available. Nearly half of the maiden ewes responded to the ‘ram effect,’ while 24% exhibited spontaneous reproductive activity and displayed oestrus during the first 14 days following the introduction of rams. Nearly 5% of females did not exhibit oestrus, and these animals are proposed for early culling. Average annual values of Normalised Difference Vegetation Index (NDVI), reflecting vegetation cover, and the percent of maiden ewes spontaneously cycling were positively correlated (P = 0.006). Interestingly, NDVI was negatively correlated with the percentage of anoestrus females that did not respond to the ‘ram effect’ but exhibited oestrus beyond the hypothetical time frame commonly used to describe it. Average fertility was 82.8% and was significantly affected by mating year, live weight at mating, and the response to the ‘ram effect.’ The highest fertility (88.39%) was for females spontaneously cycling at the time of ram introduction, and the lowest (83.35%) was for females coming into oestrus beyond the time frame for a ram-induced oestrus and ovulation. Heritability from a univariate logit-transformed analysis for fertility was 0.10 and the genetic correlation between fertility and the interval between ram introduction and oestrus was 0.26, suggesting that a shorter interval is associated with higher fertility. Thus, the interval between ram introduction and oestrus is a good candidate for selective breeding for high fertility of maiden ewes in drylands mated out-of-season using the ‘ram effect.’

The United Nations has underscored the critical role of digital connectivity by integrating it into several sustainable development goals, with the ambition for nations worldwide to achieve comprehensive access by 2030. Over thousand million people, primarily in rural areas, are disconnected from the digital world, highlighting the urgent need for viable and sustainable telecommunications solutions. These areas are characterized by sparse populations and lower economic levels, presenting great challenges for connectivity. This work introduces a strategy for enhancing rural telecommunication planning using geospatial and remote sensing data, deep learning-based clustering techniques, network graphs, and terrain analysis. The objective is to develop an optimal network topology and identify prime locations for telecommunications infrastructure, such as towers or relay stations. The methodology begins with the application of an adapted Deep Embedded Clustering (DEC) technique to identify community boundaries accurately. Then, it combines geospatial data (such as roads, terrain slope, flatness, etc.) and remote sensing data (vegetation, waterways, etc.) through an optimization algorithm. This process aims to determine the most suitable sites for infrastructure placement and the best network topology for connecting these areas. The study focuses on the region of Congo, offering a detailed case study on the application of this approach. Experimental results are presented to demonstrate the effectiveness of the proposed telecommunications expansion strategy.

Abstract Lung cancer is one of the most common type of cancer and, despite significant advances in screening and diagnosis approaches, a large proportion of patients at diagnosis still present advanced stages of the disease with distant metastasis and bad prognosis. Finding and validating biomarkers of lung cancer is therefore essential. Such studies are often conducted on European, American and Asian populations and the relevance of these biomarkers in other populations remains less clear. In that prospect, we investigated the expression level of seven microRNAs, chosen from the medical literature (miR-16-5p, miR-92a-3p, miR-103a-3p, miR-375-3p, miR-451a, miR-520-3p and miR-let-7e-5p), in the blood of Tunisian lung cancer patients, treated or not by chemotherapy, and healthy control individuals. We found that high expression levels of circulating miR-16-5p, miR-92a-3p and miR-451a in the plasma of untreated patients discriminate them from healthy control individuals. In addition, miR-16-5p and miR-451a expression levels are significantly reduced in the plasma of chemotherapy-treated patients compared to untreated patients. Our results confirmed previous work in other populations worldwide and provide further evidence that circulating miR-16-5p, miR-92a-3p and miR-451a potentially regulate key pathways involved in the initiation and progression of cancer.

Abstract Perovskite solar cells (PSCs) are currently demonstrating tremendous potential in terms of straightforward processing, a plentiful supply of materials, and easy architectural integration, as well as high power conversion efficiency (PCE). However, the elemental composition of the widely utilized organic–inorganic halide perovskites (OIHPs) contains the hazardous lead (Pb). The presence of Pb in the PSCs is problematic because of its toxicity which may slow down or even impede the pace of commercialization. As a backup option, the scientific community has been looking for non-toxic/less-toxic elements that can replace Pb in OIHPs. Despite not yet matching the impressive results of Pb-containing OIHPs, the community is paying close attention to Pb-free materials and has seen some encouraging findings. This review evaluates the Pb-replacement with suitable elements and scrutinizes the desirable optoelectronic features of such elements in OIHPs. The fundamental features of Pb-free OIHPs together with their photovoltaic performance in the PSCs are evaluated in details. Finally, we sum up the current challenges and potential opportunities for the Pb-free OIHPs and their devices.

The Pd-catalyzed annulative π-extension of 1,8-dibromonaphthalene for the preparation of fluoranthenes in a single operation has been investigated. With specific arenes such as fluorobenzenes, the Pd-catalyzed double functionalization of C–H bonds yields the desired fluoranthenes. The reaction proceeds via a palladium-catalyzed direct intermolecular arylation, followed by a direct intramolecular arylation step. As the C–H bond activation of several benzene derivatives remains very challenging, the preparation of fluoranthenes from 1,8-dibromonaphthalene via Suzuki coupling followed by intramolecular C–H activation has also been investigated to provide a complementary method. Using the most appropriate synthetic route and substrates, it is possible to introduce the desired functional groups at positions 7–10 on fluoranthenes.

This study examined the effects of extruded linseed and algae Padina pavonica extract on the reproductive performance, milk production, and gastrointestinal health of female rabbits. Thirty-six nulliparous New Zealand White female rabbits were randomly assigned to three groups (n = 12) with different diets. The control group (CNT) received a standard diet, while the other two groups received modified isoenergetic diets in which part of the CNT diet ingredients were replaced with 5% extruded linseed (L5%) and 5% extruded linseed plus 0.2% Padina pavonica algae extract (L5%PP). The rabbits were monitored from artificial insemination until the weaning of the rabbit kits, evaluating different reproductive parameters. Our results indicate that extruded linseed and alga Padina pavonica extract did not affect the feed intake or body weight of female rabbits. Additionally, no clinically significant histological changes were observed at the gastrointestinal level. The reproductive parameters, including litter size, litter weight, and milk yield, showed no significant differences among groups. Notably, perinatal and pre-weaning mortalities were reduced in litters born to females receiving omega-3 integrated diets (p < 0.05). While these findings are promising, further studies are needed to confirm these results and explore the specific mechanisms by which omega-3 affects reproductive function and litter health.

Introduction Family caregivers of patients suffering from severe psychiatric disorders may present with health problems, lower quality of life, and painful emotions, which can seriously compromise their well-being when they do not receive appropriate professional support. Objectives The aims of this study were to assess the level of burden and resilience in family caregivers of patients with severe mental disorders and to determine associated factors. Methods We conducted a descriptive and analytical cross-sectional study among family caregivers of patients followed at the psychiatry outpatient clinic of the Hedi Chaker University Hospital in Sfax, during the period from February 2022 to July 2022. We used the Connor–Davidson Resilience Scale (CD-RISC) to assess resilience and the Zarit Burden Inventory to assess the level of burden. Higher scores indicate higher resilience and greater burden. Results The sample included 90 family caregivers of patients with severe mental disorders. The average age was 50.68. They were the parents of patients in 40% of cases. Professionally active caregivers accounted for 57.8% of cases. Thirty family caregivers had a somatic disorder history (33.3%). The median age of patients was 42 years. Ten patients (11.1%) were financially independent. The diagnosis was schizophrenia in 68.9% of cases. The mean duration of illness was 16.23 years. Irregular follow-up was noted in 10 patients (11.1%). The mean scores of the Zarit Burden scale and the CD-RISC were 41.86± 10.33 and 58.46± 9.18 respectively. Unemployed caregivers and parents experienced a higher burden (p=0.001, p=0.03 respectively). The level of burden was higher in caregivers taking care of financially dependent patients (p=0.03), with a duration of the disease greater than 15 years (p=0.04), and with irregular follow-up (p=0.008). A low level of resilience in caregivers was correlated with spousal relationship (p=0.001), cohabitation with the patient (p=0.05), widowhood (p=0.01), low level of education (p=0.02), the presence of a somatic disorder history in the caregivers (p=0.04). A negative correlation was observed between CD-RISC and Zarit scores (p=0.04; r=-0.21). Conclusions Family caregivers of mentally ill patients experienced a significant level of caregiver burden, and it was lower in caregivers with higher levels of resilience. Psycho-educational programs directed toward family caregivers are highly recommended. Disclosure of Interest None Declared

Introduction Giving care to someone who is experiencing mental distress is a difficult and challenging task that could be detrimental to the caregiver’s overall quality of life. Stigma associated with mental illness is one of the most important mental health issues faced by these caregivers. Objectives Our aims were to assess perceived stigma among family caregivers of patients with severe mental disorders and to identify its associated factors. Methods We conducted a descriptive and analytical cross-sectional study among family caregivers of patients followed at the psychiatry outpatient clinic of the Hedi Chaker University Hospital in Sfax, during the period from February 2022 to July 2022. A structured interview questionnaire was designed to collect socio-demographic data of both patients and their caregivers. We used the Stigma Devaluation Scale (SDS) to assess stigma. Results A total of 90 family caregivers of severely mentally ill patients were included: 26 men and 64 women, with an average age of 50.68 ±11.67 years. Patients’ parents accounted for 40% of family caregivers. The majority of family caregivers (83.3%) had no more than secondary education. Married people represented 70% of cases. The median age of patients was 42 years. Schizophrenia was the diagnosis in 68.9% of cases. The mean duration of illness was 16.23 years. Daily assistance lasted from 4 to 8 hours in 30% of cases and more than 8 hours in 66.7% of cases. The mean score (SDS12) for family-focused stigma was 13.12 ± 2.34 with ranges from 8 to 18. Perceived stigma scores were significantly higher among caregivers caring for non-married patients (p=0.04), with an age 8 hours per day) (p=0.05) and insufficient information about the illness (p=0.02) were significantly associated with perceived stigma. Conclusions The clinicians managing patients with severe mental disorders must focus on stigma and psychological distress among the caregivers and plan intervention strategies to reduce stigma. Disclosure of Interest None Declared