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This study investigates ceramic fused filament fabrication (CF3) 3D printing of alumina with an emphasis on the impact of particle morphology on feedstock preparation and printability. Spherical powder displayed superior flow with higher apparent density, tap density, and powder packing fraction compared to irregular powder. A 55 vol % powder loading was chosen to ensure good flowability during printing. Irregular powder-based feedstocks had 40X higher viscosity than spherical powder feedstocks at 400 s−1 shear rate, posing potential printing challenges. Slow printing speed maintained low feed-rates for consistent material flow. The debinding and sintering process produced macroscopically defect-free alumina components with relative densities above 89 % for both powder morphologies. The focus of the work is on comparing two different powder particle morphologies influencing feedstock behavior, printing fidelity, and sintered part properties.

Emerging evidence suggests a significant contribution of primary cilia to cell division and proliferation. MicroRNAs, especially miR-17, contribute to cell cycle regulation and proliferation. Recent investigations have highlighted the dysregulated expression of miR-17 in various malignancies, underlining its potential role in cancer. However, the correlation between primary cilia and miR-17 has yet to be fully elucidated. The present study examines the presence of miR-17 in primary cilia. The miR-17 expression is studied in selected ciliary protein knockdown cells. Using in situ hybridization (ISH), we identified the subcellular localization of miR-17 in both cilium and cell body. We confirmed the importance of miR-17, progesterone receptor membrane component-2 (PGRMC2), and monosialodihexosylganglioside (GM3S) in cilia formation, as shown by the significant reduction in cilia and cilia length in knockdown cells compared to control. We also demonstrated the involvement of PGRMC2, GM3S, polycystin-2 (PKD2), and miR-17 in cellular proliferation and cell growth. Our studies revealed a hyperproliferative effect in the knockdown cells compared to control cells, suggesting the regulatory roles of PGRMC2/GM3S/PKD2/miR-17 in promoting cell proliferation. Overall, our studies conclude that ciliary proteins are involved in cell division and proliferation. We further hypothesize that primary cilia can serve as compartments to store and control genetic materials, further implicating their complex involvement in cellular processes.

Abstract Osteoarthritis affects millions of people worldwide but current treatments using analgesics or anti-inflammatory drugs only alleviate symptoms of this disease. Here, we present an injectable, biodegradable piezoelectric hydrogel, made of short electrospun poly-L-lactic acid nanofibers embedded inside a collagen matrix, which can be injected into the joints and self-produce localized electrical cues under ultrasound activation to drive cartilage healing. In vitro, data shows that the piezoelectric hydrogel with ultrasound can enhance cell migration and induce stem cells to secrete TGF-β1, which promotes chondrogenesis. In vivo, the rabbits with osteochondral critical-size defects receiving the ultrasound-activated piezoelectric hydrogel show increased subchondral bone formation, improved hyaline-cartilage structure, and good mechanical properties, close to healthy native cartilage. This piezoelectric hydrogel is not only useful for cartilage healing but also potentially applicable to other tissue regeneration, offering a significant impact on the field of regenerative tissue engineering.

Background: Colorectal carcinoma is a malignant epithelial tumor originating in the large bowel. More than 90% of colorectal carcinomas (CRC) are adenocarcinomas. This being graded morphologically at H&E stain in histopathology as well-differentiated, moderately differentiated, and poorly differentiated. Aims and Objectives: The aim of this study is to assess any association with different histopathological types and extent of differentiation of CRC with change in expression of CK 7 and Cytokeratin 20 (CK20). Materials and Methods: In the present study, total 59 patients has been selected by the predetermined sampling techniques. The specimen was collected and the necessary processing was done for H&E stain and for immunohistochemistry for CK 20 and CK 7 profile. Results: This study showed that most of the well-differentiated colorectal carcinoma are CK20 (+ve)/Cytokeratin 7 (CK7) (-ve) and poorly differentiated CRC are CK20 (+) and/CK7 (+) and also CK20 (−ve)/CK7 (+ve). Conclusion: Hence, from this study, it is evident that the expression of CK 20 positivity decreasing and CK7 positivity increasing in respect of progression of well differentiated to poorly differentiated colorectal carcinoma, that is, there is a relationship of expression of CK20 and CK7 profile with the histological grading of colorectal carcinoma. Furthermore, the expression profile of CK20 and CK 7 may be of helpful while searching for unknown primary in cases of metastatic deposit.

Abstract Despite different nanomaterials were developed so far against cancer, their potential drawbacks are still scarcely considered. The off-target delivery of a therapeutic compound, as well as the non-specific uptake of these nanomaterials by healthy tissues or organs, and their potential immunogenicity are some of the major issues that still have to be faced prior to a successful clinical translation. This work aims to develop an innovative theranostic, biocompatible, and drug-loaded nanoconstruct based on Gadolinium-doped Zinc Oxide (ZnO-Gd) nanocrystals (NCs), focusing on one of the most lethal diseases, i.e., pancreatic cancer. The use of zinc oxide is motivated by the huge potential of this nanomaterial already demonstrated for in vitro and in vivo applications, while the Gadolinium doping confers magnetic properties useful for diagnostics. Furthermore, an innovative biomimetic shell is here used to coat the NCs: it is composed of a lipid bilayer made from extracellular vesicles (EVs) combined with other synthetic lipids and a peptide targeting the pancreatic tumor microenvironment. To complete the nanoconstruct therapeutic function, Gemcitabine, a first-line drug for pancreatic cancer treatment, was adsorbed on the ZnO-Gd NCs prior to the coating with the above-mentioned lipidic shell. The aim of this work is thus to strongly enhance the therapeutic capability of the final nanoconstruct, providing it with high biocompatibility, colloidal stability in biological media, efficient cargo loading and release properties, as well as active targeting for site-selective drug delivery. Furthermore, the magnetic properties of the ZnO-Gd NCs core can in future allow efficient in situ bioimaging capabilities based on Magnetic Resonance Imaging technique. The obtained nanoconstructs were tested on two different pancreatic cancer cell lines, i.e., BxPC-3 and the metastatic AsPC-1, proving high cell internalization levels, mediated by the targeting peptide exposed on the nanoconstruct. Cellular cytotoxicity assay performed on both cell lines dictated ~ 20% increased cell killing efficacy of Gemcitabine when delivered through the nanoconstruct rather than as a free drug. Taken together, our designed theranostic nanoconstruct can have a significant impact on the standard treatment of pancreatic cancer.

Introduction: A variation in dental pain following tooth extraction and implant placement has been observed. The present study aimed to compare pain in patients undergoing tooth extraction and implant placement. Materials and Methods: Eighty-four patients underwent tooth extraction and implant placement in maxillary central incisor. Pain (VAS) was recorded at 24 h, 24 h, and 48 h. Results: The mean pain value (VAS) at 24 h post-operatively after tooth extraction was 6.1 and after implant insertion was 2.9. At 48 h after tooth extraction was 4.3 and after implant insertion was 1.1 and after 72 h after tooth extraction was 2.4 and after implant insertion was 0.27. A significant difference was observed between both procedures at different intervals of time (P < 0.05). Conclusion: The pain experienced by patients during dental implant insertion was comparatively less as compared to dental tooth extraction.

Abstract Deep neural networks (DNNs) have shown success in image classification, with high accuracy in recognition of everyday objects. Performance of DNNs has traditionally been measured assuming human accuracy is perfect. In specific problem domains, however, human accuracy is less than perfect and a comparison between humans and machine learning (ML) models can be performed. In recognising everyday objects, humans have the advantage of a lifetime of experience, whereas DNN models are trained only with a limited image dataset. We have tried to compare performance of human learners and two DNN models on an image dataset which is novel to both, i.e. histological images. We thus aim to eliminate the advantage of prior experience that humans have over DNN models in image classification. Ten classes of tissues were randomly selected from the undergraduate first year histology curriculum of a Medical School in North India. Two machine learning (ML) models were developed based on the VGG16 (VML) and Inception V2 (IML) DNNs, using transfer learning, to produce a 10-class classifier. One thousand (1000) images belonging to the ten classes (i.e. 100 images from each class) were split into training (700) and validation (300) sets. After training, the VML and IML model achieved 85.67 and 89% accuracy on the validation set, respectively. The training set was also circulated to medical students (MS) of the college for a week. An online quiz, consisting of a random selection of 100 images from the validation set, was conducted on students (after obtaining informed consent) who volunteered for the study. 66 students participated in the quiz, providing 6557 responses. In addition, we prepared a set of 10 images which belonged to different classes of tissue, not present in training set (i.e. out of training scope or OTS images). A second quiz was conducted on medical students with OTS images, and the ML models were also run on these OTS images. The overall accuracy of MS in the first quiz was 55.14%. The two ML models were also run on the first quiz questionnaire, producing accuracy between 91 and 93%. The ML models scored more than 80% of medical students. Analysis of confusion matrices of both ML models and all medical students showed dissimilar error profiles. However, when comparing the subset of students who achieved similar accuracy as the ML models, the error profile was also similar. Recognition of ‘stomach’ proved difficult for both humans and ML models. In 04 images in the first quiz set, both VML model and medical students produced highly equivocal responses. Within these images, a pattern of bias was uncovered–the tendency of medical students to misclassify ‘liver’ tissue. The ‘stomach’ class proved most difficult for both MS and VML, producing 34.84% of all errors of MS, and 41.17% of all errors of VML model; however, the IML model committed most errors in recognising the ‘skin’ class (27.5% of all errors). Analysis of the convolution layers of the DNN outlined features in the original image which might have led to misclassification by the VML model. In OTS images, however, the medical students produced better overall score than both ML models, i.e. they successfully recognised patterns of similarity between tissues and could generalise their training to a novel dataset. Our findings suggest that within the scope of training, ML models perform better than 80% medical students with a distinct error profile. However, students who have reached accuracy close to the ML models, tend to replicate the error profile as that of the ML models. This suggests a degree of similarity between how machines and humans extract features from an image. If asked to recognise images outside the scope of training, humans perform better at recognising patterns and likeness between tissues. This suggests that ‘training’ is not the same as ‘learning’, and humans can extend their pattern-based learning to different domains outside of the training set.

This study describes a new method for gripping and sensing a physical object (or material) with a prosthetic arm that uses haptic technologies, kinaesthetic communication, and machine learning. Haptic technology is a method of determining if an object is firm or soft as if it were gripped by a human and determining how much gripping force the object can withstand without crushing it. The bending moment and gripping force are measured using a flex sensor in human fingers and a pressure sensor applied by the tip of the human fingers. Three different types of objects (soft sponge, hard sponge, and plastic) are studied and tested in this work by pressing them with varying gripping pressures (soft, firm, and firmer). In addition, a model (Haptic Intelligence Recorder arm) is proposed that can anticipate the object type and gripping force based on the recorded intelligence data. The major goal is to educate our prosthetic hand to be able to grip various items with varying finger pressures, much like we can do naturally. Finally, a glove is created that is tailored to the intelligence arm’s ability to anticipate grabbing items.

Statistical agencies often publish microdata or synthetic data to protect confidentiality of survey respondents. This is more prevalent in case of income data. In this paper, we develop likelihood-based finite sample inferential methods for a singly imputed synthetic data using plug-in sampling and posterior predictive sampling techniques under Pareto distribution, a well known income distribution. The estimators are constructed based on sufficient statistics and the estimation methods possess desirable properties. For example, the estimators are unbiased and confidence intervals developed are exact. An extensive simulation study is carried out to analyze the performance of the proposed methods.