Introduction of Traditional Chinese Medicine (TCM) and biotechnology has great potential for personalized healthcare, where the old methods of diagnosis are incorporated with the new biotechnological innovations. Nonetheless, the merging of the two areas presents some of the most decisive issues, particularly concerning data security and interoperability. This paper presents a new framework that safely incorporates TCM and biotechnology in personalized healthcare, maintains privacy, integrity, and free flow of data. The research system employs highly sophisticated security tools, such as encryption, multi-factor authentication, and blockchain technology, to protect sensitive health information. In order to test the effectiveness of the proposed framework, performed comprehensive experiments with simulated healthcare datasets. According to the findings of the statistical analysis, the proposed system enhances the level of security and has a high security breach detection rate of 98.5 and privacy preservation efficiency of 93.2. Besides, study system realized a 25% decrease in the risk of data breach as compared to the traditional model. The rate of interoperability between data was also found to be 87, which ensured an easy integration of the TCM diagnostic systems with the biotechnological platforms. These results indicate the capacity of the framework to strike a balance between high-level security and effective data interoperability to offer a dependable solution toward ensuring the safety of the integration of TCM and biotechnology in personalized healthcare. This model helps to promote human health and well-being through the ability to provide secure cooperation of Traditional Chinese Medicine and biotechnology in individualized healthcare by providing better access to high-quality health services with preserving privacy and safety. The work presented in this research paper can add value to the field by providing a safe and viable method of integrating conventional medicine with the current biotechnological procedures. The future studies will be devoted to the clinical validation of the framework and the flexibility of the framework within the real healthcare environment.

Deer Horn Glue is rich in collagen- and tissue-derived proteins, but the link between its experimentally observed proteome, the theoretical peptide sequence space derived from that proteome, and bone-related molecular targets remains poorly defined. This study established a proteomics-guided multiscale computational workflow to prioritize putative receptor activator of nuclear factor-κB ligand (RANKL)-binding peptide candidates generated from proteins identified in one Deer Horn Glue sample. The Deer Horn Glue proteome was characterized by liquid chromatography–tandem mass spectrometry (LC-MS/MS). Experimentally identified proteins were subjected to in silico tryptic digestion and stepwise activity, safety, and physicochemical screening. All 49 retained candidates underwent global and interface-focused docking. Five prioritized peptides were examined by AlphaFold 3, three independently seeded 200 ns molecular dynamics simulations per complex, entropy-omitted molecular mechanics/generalized Born surface area (MM/GBSA) analysis, residue decomposition, and locally relaxed computational alanine substitution analysis. Proteomic analysis retained 707 target protein groups and generated 24,575 nonredundant theoretical peptide sequences. Stepwise screening retained 49 candidates. Global and interface-focused docking rankings showed modest agreement, and the expanded interface analysis identified additional candidates while retaining GASLQDWDFGK as the top-ranked sequence. Across three independent simulations, GASLQDWDFGK showed consistently low peptide root-mean-square deviation (RMSD), whereas HEFSVDMTCEGCSNAVTR formed the largest mean number of interfacial hydrogen bonds. Their entropy-omitted MM/GBSA estimates were generally the most favorable, although the order varied among simulations. Locally relaxed alanine substitutions highlighted reproducible energy-sensitive positions for experimental testing. By linking an experimentally observed Deer Horn Glue proteome to multiscale structural analysis, this workflow provides a traceable and reproducible strategy for prioritizing testable peptide–RANKL interaction hypotheses. The five candidates remain theoretical products of in silico digestion and require targeted detection, direct binding, and functional validation.

The increasing availability of large volumes of biological data and the digitization of classical medical knowledge are rare chances to enhance medical research through integrative analysis. But the synergy between bioinformatics and Traditional Chinese Medicine (TCM) has not proven to be fruitful, thus restricting the potential of bioinformatics in providing holistic interpretation and decision support. In the current paper, a cloud-based platform that combines bioinformatics and TCM is presented and used to exchange and analyze heterogeneous datasets seamlessly. The framework integrates bioinformatics numerical data with symbolic TCM knowledge, which uses preprocessing, semantic mapping, and hybrid analysis layers to assist in offering better decision support. The experimental testing on secondary data, including gene expression data, disease-gene relationship data, and digitized TCM knowledge, shows that all the patterns improve pattern consistency (0.68 to 0.81), interpretability (0.62 to 0.78), analytical stability (0.65 to 0.83), and knowledge alignment accuracy (0.50 to 0.86). This makes for a complementary way of enhancing the interpretability of molecular data using TCM patterns of diagnosis, thus forming an effective model of healthcare research. This cloud computing framework enhances the well-being of humanity as a result of cooperation between bioinformatics and TCM, and it also leads to improved decision support and research in healthcare based on data. The research paper presents some possibilities for leveraging cloud computing technology to make valuable contributions to clinical practices, ontologies, and data-driven healthcare research.