Does Exam-targeted Training Help Village Doctors Pass the Certified (Assistant) Physician Exam and Improve Their Practical Skills? A Cross-sectional Analysis of Village Doctors\u27 Perspectives in Changzhou in Eastern China

Background Quality of health care needs to be improved in rural China. The Chinese government, based on the 1999 Law on Physicians, started implementing the Rural Doctor Practice Regulation in 2004 to increase the percentage of certified physicians among village doctors. Special exam-targeted training for rural doctors therefore was launched as a national initiative. This study examined these rural doctors’ perceptions of whether that training helps them pass the exam and whether it improves their skills. Methods Three counties were selected from the 4 counties in Changzhou City in eastern China, and 844 village doctors were surveyed by a questionnaire in July 2012. Chi-square test and Fisher exact test were used to identify differences of attitudes about the exam and training between the rural doctors and certified (assistant) doctors. Longitudinal annual statistics (1980–2014) of village doctors were further analyzed. Results Eight hundred and forty-four village doctors were asked to participate, and 837 (99.17%) responded. Only 14.93% of the respondents had received physician (assistant) certification. Only 49.45% of the village doctors thought that the areas tested by the certification exam were closely related to the healthcare needs of rural populations. The majority (86.19%) felt that the training program was “very helpful” or “helpful” for preparing for the exam. More than half the village doctors (61.46%) attended the “weekly school”. The village doctors considered the most effective method of learning was “continuous training (40.36%)” . The majority of the rural doctors (89.91%) said they would be willing to participate in the training and 96.87% stated that they could afford to pay up to 2000 yuan for it. Conclusions The majority of village doctors in Changzhou City perceived that neither the certification exam nor the training for it are closely related to the actual healthcare needs of rural residents. Policies and programs should focus on providing exam-preparation training for selected rural doctors, reducing training expenditures, and utilizing web-based methods. The training focused on rural practice should be provided to all village doctors, even certified physicians. The government should also adjust the local licensing requirements to attract and recruit new village doctors

S2vNTM: Semi-supervised vMF Neural Topic Modeling

Language model based methods are powerful techniques for text classification. However, the models have several shortcomings. (1) It is difficult to integrate human knowledge such as keywords. (2) It needs a lot of resources to train the models. (3) It relied on large text data to pretrain. In this paper, we propose Semi-Supervised vMF Neural Topic Modeling (S2vNTM) to overcome these difficulties. S2vNTM takes a few seed keywords as input for topics. S2vNTM leverages the pattern of keywords to identify potential topics, as well as optimize the quality of topics' keywords sets. Across a variety of datasets, S2vNTM outperforms existing semi-supervised topic modeling methods in classification accuracy with limited keywords provided. S2vNTM is at least twice as fast as baselines.Comment: 17 pages, 9 figures, ICLR Workshop 2023. arXiv admin note: text overlap with arXiv:2307.0122

Formation of elongated starch granules in high-amylose maize

GEMS-0067 maize starch contains up to 32% elongated starch granules, much higher than amylose-extender (ae) single-mutant maize starch (∼7%) and normal (non-mutant) maize starch (0%). These elongated granules are highly resistant to enzymatic hydrolysis at 95–100 °C, which function as resistant starch. The structure and formation of these elongated starch granules, however, were not known. In this study, light, confocal laser-scanning, scanning electron, and transmission electron microscopy were used to reveal the structure and formation of these elongated starch granules. The transmission electron micrographs showed fusion through amylose interaction between adjacent small granules in the amyloplast at the early stage of granule development. A mechanistic model for the formation of elongated starch granules is proposed

An End-to-end In-Silico and In-Vitro Drug Repurposing Pipeline for Glioblastoma

Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive brain cancer with poor prognosis. We propose a computational framework that utilizes machine learning-based propensity score matching to estimate counterfactual treatment effects and predict synergistic effects of drug combinations. Through our in-silico analysis, we identified promising drug candidates and drug combinations that warrant further investigation. To validate these computational findings, we conducted in-vitro experiments on two GBM cell lines, U87 and T98G. The experimental results demonstrated that some of the identified drugs and drug combinations indeed exhibit strong suppressive effects on GBM cell growth. Our end-to-end pipeline showcases the feasibility of integrating computational models with biological experiments to expedite drug repurposing and discovery efforts. By bridging the gap between in-silico analysis and in-vitro validation, we demonstrate the potential of this approach to accelerate the development of novel and effective treatments for glioblastoma

An End-to-end In-Silico and In-Vitro Drug Repurposing Pipeline for Glioblastoma

Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive brain cancer with poor prognosis. We propose a computational framework that utilizes machine learning-based propensity score matching to estimate counterfactual treatment effects and predict synergistic effects of drug combinations. Through our in-silico analysis, we identified promising drug candidates and drug combinations that warrant further investigation. To validate these computational findings, we conducted in-vitro experiments on two GBM cell lines, U87 and T98G. The experimental results demonstrated that some of the identified drugs and drug combinations indeed exhibit strong suppressive effects on GBM cell growth. Our end-to-end pipeline showcases the feasibility of integrating computational models with biological experiments to expedite drug repurposing and discovery efforts. By bridging the gap between in-silico analysis and in-vitro validation, we demonstrate the potential of this approach to accelerate the development of novel and effective treatments for glioblastoma

A conserved morphogenetic mechanism for epidermal ensheathment of nociceptive sensory neurites.

Interactions between epithelial cells and neurons influence a range of sensory modalities including taste, touch, and smell. Vertebrate and invertebrate epidermal cells ensheath peripheral arbors of somatosensory neurons, including nociceptors, yet the developmental origins and functional roles of this ensheathment are largely unknown. Here, we describe an evolutionarily conserved morphogenetic mechanism for epidermal ensheathment of somatosensory neurites. We found that somatosensory neurons in Drosophila and zebrafish induce formation of epidermal sheaths, which wrap neurites of different types of neurons to different extents. Neurites induce formation of plasma membrane phosphatidylinositol 4,5-bisphosphate microdomains at nascent sheaths, followed by a filamentous actin network, and recruitment of junctional proteins that likely form autotypic junctions to seal sheaths. Finally, blocking epidermal sheath formation destabilized dendrite branches and reduced nociceptive sensitivity in Drosophila. Epidermal somatosensory neurite ensheathment is thus a deeply conserved cellular process that contributes to the morphogenesis and function of nociceptive sensory neurons

Recovering Sign Bits of DCT Coefficients in Digital Images as an Optimization Problem

Recovering unknown, missing, damaged, distorted or lost information in DCT coefficients is a common task in multiple applications of digital image processing, including image compression, selective image encryption, and image communications. This paper investigates recovery of a special type of information in DCT coefficients of digital images: sign bits. This problem can be modelled as a mixed integer linear programming (MILP) problem, which is NP-hard in general. To efficiently solve the problem, we propose two approximation methods: 1) a relaxation-based method that convert the MILP problem to a linear programming (LP) problem; 2) a divide-and-conquer method which splits the target image into sufficiently small regions, each of which can be more efficiently solved as an MILP problem, and then conducts a global optimization phase as a smaller MILP problem or an LP problem to maximize smoothness across different regions. To the best of our knowledge, we are the first who considered how to use global optimization to recover sign bits of DCT coefficients. We considered how the proposed methods can be applied to JPEG-encoded images and conducted extensive experiments to validate the performances of our proposed methods. The experimental results showed that the proposed methods worked well, especially when the number of unknown sign bits per DCT block is not too large. Compared with other existing methods, which are all based on simple error-concealment strategies, our proposed methods outperformed them with a substantial margin, both according to objective quality metrics (PSNR and SSIM) and also our subjective evaluation. Our work has a number of profound implications, e.g., more sign bits can be discarded to develop more efficient image compression methods, and image encryption methods based on sign bit encryption can be less secure than we previously understood.Comment: 13 pages, 8 figure

Bioengineered Cell Niche for Skeletal Muscle Regeneration

Skeletal muscles can self-repair minor strains, lacerations, and contusions; however, in cases of volumetric muscle lossand muscle degenerative diseases, tissue fails to regenerate. Current cell-based therapies, such as myoblast transplantation, have significant drawbacks of low survival rates and engraftment efficacy, mainly due to the absence of supportive cell microenvironment. Scaffolds that mimic the natural cell microenvironment provide a robust platform to support cell adhesion, migration, proliferation, and differentiation. Electrospinning is a versatile technology platform used for fabricating the fiber scaffold that mimics the extracellular matrix. Thus, we aim to reconstitute the cell microenvironment through development of aligned fiber scaffolds by electrospinning as oriented muscle fibers create natural microenvironment of myogenic cells. In particular, aligned fiber scaffolds will be optimized in term of mechanical properties and fiber diameters as fiber curvature and mechanical stiffness provide significant physical cues for myogenic cell behaviors. Here, we fabricated and characterized electrospun polyester fiber scaffolds with different diameters from micro-scale to nano-scale. The mechanical properties of the fabricated nanofibers were found to be in the range of contractile muscles as evidenced from atomic force microscopy measurements. With these scaffolds, C2C12 myoblasts were seeded and analyzed for the initial attachment. It was shown that aligned fibers with varying diameters resulted in different responses in cell attachment, indicating the role of cell topography sensing in cell-biomaterial interactions. Current ongoing studies focus on long-term in vitro culture of scaffolds in a custom-made muscle bioreactor emulating the contraction/relaxation of skeletal muscle tissue