MedChatZH: a Better Medical Adviser Learns from Better Instructions

Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems. However, in specialized domains like traditional Chinese medical QA, these models may perform unsatisfactorily without fine-tuning on domain-specific datasets. To address this, we introduce MedChatZH, a dialogue model designed specifically for traditional Chinese medical QA. Our model is pre-trained on Chinese traditional medical books and fine-tuned with a carefully curated medical instruction dataset. It outperforms several solid baselines on a real-world medical dialogue dataset. We release our model, code, and dataset on https://github.com/tyang816/MedChatZH to facilitate further research in the domain of traditional Chinese medicine and LLMs.Comment: 7 pages, 3 figure

Discovering medication patterns for high-complexity drug-using diseases through electronic medical records

An Electronic Medical Record (EMR) is a professional document that contains all data generated during the treatment process. The EMR can utilize various data formats, such as numerical data, text, and images. Mining the information and knowledge hidden in the huge amount of EMR data is an essential requirement for clinical decision support, such as clinical pathway formulation and evidence-based medical research. In this paper, we propose a machine-learning-based framework to mine the hidden medication patterns in EMR text. The framework systematically integrates the Jaccard similarity evaluation, spectral clustering, the modified Latent Dirichlet Allocation and cross-matching among multiple features to find the residuals that describe additional knowledge and clusters hidden in multiple perspectives of highly complex medication patterns. These methods work together, step by step to reveal the underlying medication pattern. We evaluated the method by using real data from EMR text (patients with cirrhotic ascites) from a large hospital in China. The proposed framework outperforms other approaches for medication pattern discovery, especially for this disease with subtle medication treatment variances. The results also revealed little overlap among the discovered patterns; thus, the distinct features of each pattern are well studied through the proposed framework

The serum IgG antibody level as a biomarker for clinical outcome in patients with cerebral sparganosis after treatment

IntroductionCerebral sparganosis is a rare parasitic infection of the brain tissue. The remission of MRI change and clinical symptom has been used to evaluate the therapeutic effect. However, there is no study to correlate the serum IgG antibody level of sparganum to the prognosis of disease after treatment. Methods87 patients with cerebral sparganosis were collected from three medical centers. Clinical symptoms and MRI changes were evaluated at 12 months after initial treatment, and serum IgG antibody level of sparganum was evaluated at 2, 6, and 12 months after treatment. The positive cut-off value was based on 2.1 times the optical density (OD) of negative control. The index value was defined as the sample OD divided by the cut-off value.ResultsAmong the 87 patients after treatment, 71 patients had good clinical outcomes, and 16 had poor clinical outcomes. The area under the curve (AUC) showed that the index value measured at 12 months after treatment had the best prediction effect, with a value of 2.014. In the good-outcome group, the index values were less than 2.014 in all 71 patients, and only 8 patients had mildly enhanced residual lesions on MRI. In the poor-outcome group, the index values were more than 2.014 in all 16 patients, and all patients still showed significantly enhanced lesions on MRI. Compared with poor-outcome patients, only 2 patients with good outcomes had disease recurrence after treatment.DiscussionThis study provided evidence that the serum IgG antibody level of sparganum was a promising biomarker to evaluate the prognosis of patients with cerebral sparganosis after treatment

Some Picture Fuzzy Dombi Heronian Mean Operators with Their Application to Multi-Attribute Decision-Making

As an extension of the intuitionistic fuzzy set (IFS), the recently proposed picture fuzzy set (PFS) is more suitable to describe decision-makers’ evaluation information in decision-making problems. Picture fuzzy aggregation operators are of high importance in multi-attribute decision-making (MADM) within a picture fuzzy decision-making environment. Hence, in this paper our main work is to introduce novel picture fuzzy aggregation operators. Firstly, we propose new picture fuzzy operational rules based on Dombi t-conorm and t-norm (DTT). Secondly, considering the existence of a broad and widespread correlation between attributes, we use Heronian mean (HM) information aggregation technology to fuse picture fuzzy numbers (PFNs) and propose new picture fuzzy aggregation operators. The proposed operators not only fuse individual attribute values, but also have a good ability to model the widespread correlation among attributes, making them more suitable for effectively solving increasingly complicated MADM problems. Hence, we introduce a new algorithm to handle MADM based on the proposed operators. Finally, we apply the newly developed method and algorithm in a supplier selection issue. The main novelties of this work are three-fold. Firstly, new operational laws for PFSs are proposed. Secondly, novel picture fuzzy aggregation operators are developed. Thirdly, a new approach for picture fuzzy MADM is proposed

Securing Personal Health Records in the Cloud by Enforcing Sticky Policies

The personal health records (PHR) always contain much health-related privacy information in different categories. When storing the PHR data in the cloud, the PHR owner loses control to the sensitive information and is confronted with potential privacy exposure. In this paper, we propose a scheme to enable the protection of the PHR data hosted in the cloud. It not only supports that the data access can be fine-grained and base on the privacy policies specified by the PHR owner, but also affords an effective encryption mechanism and flexible key management approach to enforce the privacy policies sticky to the PHR data. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.240

Ecological Factors and Anthropogenic Disturbance May Restructure the Skin Microbiota of Maoershan Hynobiids (<i>Hynobius maoershanensis</i>)

Studies on the skin microbiota of amphibians in different disturbed habitats can clarify the relationship between the skin microbiota composition and environmental factors and have practical implications for the conservation of endangered species. In this study, 16S rRNA high-throughput sequencing was used to profile the skin microbiota of Maoershan hynobiids (Hynobius maoershanensis). Our results illustrate that the alpha diversity of the skin microbiota significantly differed among individuals in higher anthropogenic disturbance-degree (HADD) habitats and lower anthropogenic disturbance-degree (LADD) habitats. The diversity of the skin microbiota in forelimb bud-stage tadpoles from HADD habitats was higher than that in their counterparts from LADD habitats. The richness of the skin microbiota in hindlimb bud-stage tadpoles was greater in HADD habitats than in LADD habitats. However, the alpha diversity of the adult skin microbiota did not differ significantly between the two habitats. Furthermore, stepwise regression analysis indicated that the skin microbiota diversity and relative abundance of dominant bacteria decreased with increasing air temperature, water temperature, and pH; conversely, skin microbiota richness increased with increasing humidity. In addition, the relative abundance of dominant bacteria was influenced by anthropogenic disturbance. We conclude that the skin microbiota of Maoershan hynobiids is affected by ecological factors and anthropogenic disturbance, highlighting the importance of the skin microbiota in response to habitat alteration

Some q-Rung Dual Hesitant Fuzzy Heronian Mean Operators with Their Application to Multiple Attribute Group Decision-Making

The q-rung orthopair fuzzy sets (q-ROFSs), originated by Yager, are good tools to describe fuzziness in human cognitive processes. The basic elements of q-ROFSs are q-rung orthopair fuzzy numbers (q-ROFNs), which are constructed by membership and nonmembership degrees. As realistic decision-making is very complicated, decision makers (DMs) may be hesitant among several values when determining membership and nonmembership degrees. By incorporating dual hesitant fuzzy sets (DHFSs) into q-ROFSs, we propose a new technique to deal with uncertainty, called q-rung dual hesitant fuzzy sets (q-RDHFSs). Subsequently, we propose a family of q-rung dual hesitant fuzzy Heronian mean operators for q-RDHFSs. Further, the newly developed aggregation operators are utilized in multiple attribute group decision-making (MAGDM). We used the proposed method to solve a most suitable supplier selection problem to demonstrate its effectiveness and usefulness. The merits and advantages of the proposed method are highlighted via comparison with existing MAGDM methods. The main contribution of this paper is that a new method for MAGDM is proposed

Mesosphere of Carbon-Shelled Copper Nanoparticles with High Conductivity and Thermal Stability via Direct Carbonization of Polymer Soft Templates

Copper nanoparticle (Cu NP) is a promising replacement for noble metal nanoparticles due to its high electrical conductivity and low cost. However, Cu NPs are relatively active compared to noble metals, and current ways of protecting Cu NPs from oxidation by encapsulation have severe drawbacks, such as a long reaction time and complicated processes. Here, a facial and effective method to prepare the mesosphere of carbon-shelled copper nanoparticles (Cu@MC) was demonstrated, and the resulting Cu@MC was both highly electrically conductive and thermally stable. Cu@organic (100 nm) was first synthesized by the reduction of Cu ions with poly (vinyl pyrrolidone) (PVP) and sodium poly ((naphthalene-formaldehyde) sulfonate) (Na-PNFS) as soft templates. Then, the carbon shells were obtained by in situ carbonization of the polymer soft templates. The Cu@organic and Cu@MC showed an anti-oxidation ability up to 175 and 250 °C in the air atmosphere, respectively. Furthermore, the Cu@MC exhibited excellent volume resistivity of 7.2 × 10−3 Ω·cm under 20 MPa, and showed promising application potential in electric sensors and devices

Narrowband photoblinking InP/ZnSe/ZnS quantum dots for super-resolution multifocal structured illumination microscopy enhanced by optical fluctuation

Cadmium-free quantum-dot (QD) fluorophores can bridge the gap between the macroscopic and microscopic domains in fluorescence super-resolution bioimaging. InP/ZnSe/ZnS QD photoblinking fluorescent probes can improve the performance of reactive super-resolution imaging techniques and spontaneously switch fluorophores between at least two states (open and close) without depending on intense laser light and specialized buffers for bioimaging. Multifocal structured illumination microscopy (MSIM) provides a two-fold resolution enhancement in sub-diffraction imaging, but higher resolutions are limited by the pattern frequency and signal-to-noise ratio. We exploit the synergy between MSIM and spontaneously switching InP/ZnSe/ZnS QD fluorophores to further increase the imaging resolution. We demonstrate the experimental combination of optical-fluctuation-enhanced super-resolution MSIM using ultrasonic-oscillation-assisted organic solvothermal synthesis of narrowband photoblinking InP/ZnSe/ZnS QDs. The InP/ZnSe/ZnS QDs show a monodisperse grain size of approximately 9 nm, fluorescence quantum yields close to 100%, and full width at half maximum below 30 nm. The structural, electronic, and optical properties are characterized through experiments and first-principles calculations. The enhanced MSIM imaging achieves an approximate fourfold improvement in resolution for fixed cells compared with widefield imaging. The proposed InP/ZnSe/ZnS QD fluorescent probes seem promising for super-resolution imaging using MSIM