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Background: Synovial chondromatosis is an uncommon metaplastic process of the synovial lining that results in the formation of cartilaginous nodules within joints or their associated bursae or tendon sheaths. Radiologic evidence of mineralized bodies within these structures is typically pathognomonic for this condition. Extraarticular chondromatosis is rarer than intraarticular chondromatosis, and the knee is affected less frequently than the smaller joints of the hands and feet. To our knowledge, no reports describing this condition in the semimembranosus-medial collateral ligament (SM-MCL) bursa have been published. Case presentation: We describe a case of tenosynovial chondromatosis in a 37-year-old woman. The case was atypical for both the location within the SM-MCL bursa and the paucity of radiodense or hypointense changes to support a clinical suspicion of chondroid metaplasia on radiographs and T2-weighted MRI, respectively. Recreational weightlifting and swimming by the patient were impaired by chronic pain, and restricted range of motion of the ipsilateral knee persisted despite extensive skilled physical therapy and injections of both corticosteroids and platelet-rich plasma. Thirteen months after a diagnostic and therapeutic knee arthroscopy, open surgical excision of the SM-MCL bursal body was performed, and knee pain and range of motion improved by the 6-week postoperative reevaluation. Pathologic evaluation of the excised tissue was consistent with tenosynovial chondromatosis. Conclusions: Synovial chondromatosis should be considered in the differential diagnosis for recalcitrant bursitis, even in the absence of classic imaging findings. Level of evidence: 4. [ABSTRACT FROM AUTHOR]

Releasing a protein according to a zero-order profile without protein denaturation during the polymeric microparticle degradation process is very challenging. The aim of the current study was to develop protein-loaded microspheres with new PLGA based penta-block copolymers for a linear sustained protein release. Lysozyme was chosen as model protein and 40 µm microspheres were prepared using the solid-in-oil-in-water solvent extraction/evaporation process. Two types of PLGA-P188-PLGA penta-block copolymers were synthetized with two PLGA-segments molecular weight (20 kDa or 40 kDa). The resulting microspheres (50P20-MS and 50P40-MS) had the same size, an encapsulation efficiency around 50–60% but different porosities. Their protein release profiles were complementary: linear but non complete for 50P40-MS, non linear but complete for 50P20-MS. Two strategies, polymer blending and microsphere mixing, were considered to match the release to the desired profile. The (1:1) microsphere mixture was successful. It induced a bi-phasic release with a moderate initial burst (around 13%) followed by a nearly complete linear release for 8 weeks. This study highlighted the potential of this penta-block polymer where the PEO block mass ratio influence clearly the Tg and consequently the microsphere structure and the release behavior at 37 °C. The (1:1) mixture was a starting point but could be finely tuned to control the protein release. [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract Different types of biodegradable nanoparticles (NP) have been studied as nasal mucosa cell delivery systems. These nanoparticles need to strongly interact with mucosa cells to deliver their payload. However, only a few simultaneous comparisons have been made and it is therefore difficult to determine the best candidate. Here we compared 5 types of nanoparticles with different surface charge (anionic or cationic) and various inner compositions as potential vectors: cationic and anionic liposomes, cationic and anionic PLGA (Poly Lactic co-Glycolic Acid) NP and porous and cationic maltodextrin NP (cationic surface with an anionic lipid core: NPL). We first quantified their nasal residence time after nasal administration in mice using in vivo live imaging and NPL showed the longest residence time. In vitro endocytosis on mucosal cells (airway epithelial cells, macrophages and dendritic cells) using labeled nanoparticles were performed by flow cytometry and confocal microscopy. Among the 5 nanoparticles, NPL were taken up to the greatest extent by the 3 different cell lines and the endocytosis mechanisms were characterized. Taken together, we observed that the nanoparticles' cationic surface charge is insufficient to improve mucosal residence time and cellular uptake and that the NPL are the best candidates to interact with airway mucosal cells. [ABSTRACT FROM AUTHOR]

The coronavirus 2019 pandemic has resulted in a surge of patients with acute respiratory distress syndrome. Prone positioning may be used in such patients to optimize oxygenation. Severe infections may leave survivors with significant functional impairment necessitating rehabilitation. Those who have experienced prolonged prone positioning are at increased risk for complications not typically associated with critical illness. This case report describes the course and clinical findings of a survivor of acute respiratory distress syndrome due to coronavirus 2019 who was prone positioned while in intensive care and subsequently admitted to an inpatient rehabilitation facility. Her related complications, as well as those described in the literature, are reviewed. Critical elements of a comprehensive rehabilitation treatment plan for those who have been prone positioned, including implementation of preventive strategies, as well as early recognition and treatment of related injuries, will be described. [ABSTRACT FROM AUTHOR]

The retrieval of 3D objects has gained significant importance in recent years due to its broad range of applications in computer vision, computer graphics, virtual reality, and augmented reality. However, the retrieval of 3D objects presents significant challenges due to the intricate nature of 3D models, which can vary in shape, size, and texture, and have numerous polygons and vertices. To this end, we introduce a novel SHREC challenge track that focuses on retrieving relevant 3D animal models from a dataset using sketch queries and expedites accessing 3D models through available sketches. Furthermore, a new dataset named ANIMAR was constructed in this study, comprising a collection of 711 unique 3D animal models and 140 corresponding sketch queries. Our contest requires participants to retrieve 3D models based on complex and detailed sketches. We receive satisfactory results from eight teams and 204 runs. Although further improvement is necessary, the proposed task has the potential to incentivize additional research in the domain of 3D object retrieval, potentially yielding benefits for a wide range of applications. We also provide insights into potential areas of future research, such as improving techniques for feature extraction and matching and creating more diverse datasets to evaluate retrieval performance. • ANIMAR dataset was constructed with 711 3D animal models and 140 sketch queries. • SHREC challenge track focusing on Text-based 3D ANIMAl model fine-grained Retrieval. • We receive satisfactory results from eight teams and 204 runs. [ABSTRACT FROM AUTHOR]

3D object retrieval is an important yet challenging task that has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe this task can potentially drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from fully solved. As such, we provide insights into potential areas for future research and improvements. We believe we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies. • ANIMAR dataset is created with 711 3D animal models and 150 text queries. • SHREC challenge track focusing on Text-based 3D ANIMAl model fine-grained Retrieval. • Five groups participated in our competition, submitting a total of 114 runs. • We push boundaries of 3D object retrieval and facilitate user-friendly interactions. [ABSTRACT FROM AUTHOR]

Here, we aimed to develop protein loaded microspheres (MSs) using penta-block PLGA-based copolymers to obtain sustained and complete protein release. We varied MS morphology and studied the control of protein release. Lysozyme was used as a model protein and MSs were prepared using the solid-in-oil-in-water emulsion solvent extraction method. We synthesized and studied various penta-block PLGA-based copolymers. Copolymer characteristics (LA/GA ratio and molecular weight of PLGA blocks) influenced MS morphology. MS porosity was influenced by process parameters (such as solvent type, polymer concentration, emulsifying speed), whereas the aqueous volume for extraction and stabilizer did not have a significant effect. MSs of the same size, but different morphologies, exhibited different protein release behavior, with porous structures being essential for the continuous and complete release of encapsulated protein. These findings suggest strategies to engineer the morphology of MSs produced from PLGA-based multi-block copolymers to achieve appropriate release rates for a protein delivery system. [ABSTRACT FROM AUTHOR]

The compound 6 - O - veratroyl catalpol ( 6 - O ) is a bioactive iridoid glucoside that was originally isolated from Pseudolysimachion rotundum var. subintegrum. It has been demonstrated that catapol derivative iridoid glucosides including 6 - O , possess anti-inflammatory activity in carragenan-induced paw edema mouse model as well as bronchoalveolar lavage fluid of ovalbumin-induced mouse model. In the present study, we investigated whether 6 - O modulates inflammatory responses in THP-1 monocytic cells stimulated with phorbol12-myristate-13-acetate (PMA). Our data showed that 6 - O inhibited PMA induced interleukin (IL)-1β and tumor necrosis factor (TNF)-α expression in THP-1 monocytic cells. Mechanistic studies revealed that 6 - O suppressed the activity of protein kinase C (PKC), which further resulted in downstream inactivation of extracellular signal-regulated kinase (ERK) and nuclear factor-κB (NF-κB) inflammatory pathway. The results implied that 6 - O may protect against inflammatory responses that could be a potential compound in treating inflammatory diseases. [ABSTRACT FROM AUTHOR]

Background: The shift work schedule is a common work arrangement that can disrupt typical sleep-wake rhythms and lead to negative health consequences. The present study aims to examine the effect of shift work on health-related quality of life (QoL) and explore potential behaviorial mediators (i.e., sleep, eating, exercise, smoking, drinking). Methods: A cross-sectional survey was conducted among 4,449 petroleum workers in southwest China. Data on shift work status, health behaviors, and physical and mental health QoL were collected. We tested our model using path analysis and the Monte Carlo approach among 2,129 included participants. Results: After adjusting for covariates, shift work did not exhibit a significant direct association with QoL. However, shift work indirectly related to poorer physical health quality of life via less frequent healthy food consumption; shift work also indirectly related to poorer mental health QoL via both less frequent healthy food consumption and physical exercise. No significant indirect effects were found via sleeping, smoking, or drinking. Conclusions: Results suggest that shift work presents a challenge for QoL among Chinese petroleum workers due to their lesser engagement in two specific health behaviors: healthy eating and physical exercise. Healthy eating and exercise may present an even more prominent threat to shift workers' QoL than sleep and substance use. Strategies targeting shift work schedule as well as eating and exercise behaviors may help protect against poor QoL and adverse physical and mental health outcomes in this vulnerable group. [ABSTRACT FROM AUTHOR]

This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses. The task is the detection of gestures belonging to a dictionary of 16 classes characterized by different pose and motion features. The dataset features continuous sequences of hand tracking data where the gestures are interleaved with non-significant motions. The data have been captured using the Hololens 2 finger tracking system in a realistic use-case of mixed reality interaction. The evaluation is based not only on the detection performances but also on the latency and the false positives, making it possible to understand the feasibility of practical interaction tools based on the algorithms proposed. The outcomes of the contest's evaluation demonstrate the necessity of further research to reduce recognition errors, while the computational cost of the algorithms proposed is sufficiently low. [Display omitted] • 3D Shape Retrieval Challenge 2022 at 3DOR'22 Track on Online detection of heterogeneous gestures. • New gesture dataset with 288 gestures sequences and 16 gestures dictionary. • Contest with 4 groups presenting their gesture recognition methods and different variants. • Report of results and performances for all the methods. [ABSTRACT FROM AUTHOR]

This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged object segmentation) in terms of quantity and diversity. The new dataset substantially increases the number of images with hierarchical pixel-wise ground truths. We also provide a benchmark suite for the task of camouflaged instance segmentation. In particular, we present an extensive evaluation of state-of-the-art instance segmentation methods on our newly constructed CAMO++ dataset in various scenarios. We also present a camouflage fusion learning (CFL) framework for camouflaged instance segmentation to further improve the performance of state-of-the-art methods. The dataset, model, evaluation suite, and benchmark will be made publicly available on our project page. [ABSTRACT FROM AUTHOR]

• 3D Shape Retrieval Challenge 2021 at 3DOR'21 Track on Skeleton-based Hand Gesture Recognition in the Wild. • New gesture dataset with 180 gestures sequences and 18 gestures dictionary. • Contest with 4 groups presenting their gesture recognition methods. • Report of results and performances for all the methods. [Display omitted] Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more. Recognition of hand gestures can be nowadays performed directly from the stream of hand skeletons estimated by software provided by low-cost trackers (Ultraleap) and MR headsets (Hololens, Oculus Quest) or by video processing software modules (e.g. Google Mediapipe). Despite the recent advancements in gesture and action recognition from skeletons, it is unclear how well the current state-of-the-art techniques can perform in a real-world scenario for the recognition of a wide set of heterogeneous gestures, as many benchmarks do not test online recognition and use limited dictionaries. This motivated the proposal of the SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild. For this contest, we created a novel dataset with heterogeneous gestures featuring different types and duration. These gestures have to be found inside sequences in an online recognition scenario. This paper presents the result of the contest, showing the performances of the techniques proposed by four research groups on the challenging task compared with a simple baseline method. [ABSTRACT FROM AUTHOR]

• 3D models are retrieved with respect to the reliefs impressed on their surface. • A new benchmark of 3D models with geometric reliefs derived from 11 real textures. • Analysis of the retrieval performance of methods that participated to SHREC2020. • An extensive comparison of eight methods and twenty runs. • These methods are based on features or transfer learning. This paper presents the methods that have participated in the SHREC'20 contest on retrieval of surface patches with similar geometric reliefs and the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local "geometric pattern" information, possibly forgetting model size and bending. Seven groups participated in this contest and twenty runs were submitted for evaluation. The performances of the methods reveal that good results are achieved with a number of techniques that use different approaches. [ABSTRACT FROM AUTHOR]