Applications of digital Medicine in oncology: Prospects and challenges

The current state of oncology medical services is not encouraging and is unable to fully meet the needs of patients with cancer.In recent years,rapidly developing artificial intelligence technology and gradual advancements in mobile phones,sensors,and wearable devices,which have made these more compact,affordable,and popular,have greatly expanded the development of digital medicine.Digital medicine refers to clinical evidence-based technology and products with a direct impact on disease management and research.Integrating digital medicine into clinical practice has the advantages of broader applicability,greater cost-effectiveness,better accessibility,and improved diagnostic and therapeutic performance.Digital medicine has emerged in different clinical application scenarios,including cancer prevention,screening,diagnosis,and treatment,as well as clinical trials.Additionally,big data generated from digital medicine can be used to improve levels of clinical diagnosis and treatment.However,digital medicine also faces many challenges,including security regulation and privacy protection,product usability,data management,and optimization of algorithms.In summary,the application and development of digital medicine in the field of cancer face numerous opportunities and challenges.

Application of Digital Medicine in Addiction

Digital medicine plays an important role in disease assessment,psychological intervention,and relapse management in mental illnesses.Patients with substance use disorders can be easily affected by the environment and negative emotions,inducing addiction and relapse.However,due to social discrimination,stigma,or economic issues,they are unwilling to go to the hospital for treatment,making it difficult for health workers to track their health changes.Additionally,mental health resources in China are insufficient.Digital medicine aims to solve these problems.This article reviews digital medicine in the field of addiction,hoping to provide a reference for the future exploration of more individualized and effective digital medicine.

Ophthalmic Syndrome Differentiation System and Digital Chinese Medicine

In traditional Chinese medicine(TCM),ophthalmic syndrome differentiation is an ophthalmology-specific method for identifying syndromes based on the“Five Orbiculi”theory.It was devised by Professor Qing-Hua PENG through an unprecedented combination of syndrome element differentiation and ophthalmic clinical practices,based on the Clinical Terminology of Chinese Medical Diagnosis and Treatment-Syndromes of the National Standards of the People's Republic of China.This approach integrates an ophthalmic syndrome differentiation system with digital Chinese medicine(DCM),and proposes the extraction of syndrome elements of ophthalmic diseases from research on DCM.These elements are then quantified and organized to form a model of digital diagnosis and treatment specific to ophthalmology,which should help to achieve synergistic development of the ophthalmic syndrome differentiation system and DCM.

Digital Chinese Medicine Research: an Overview

Digital Chinese medicine(DCM)is an emerging interdisciplinary field developed from traditional Chinese medicine(TCM)and information science.In this article,we consider the significance and propose an architecture for DCM research,and highlight the key tasks that need to be performed to provide references for the integration of information science into Chinese medicine research.

Thoughts About Paths for Digitization of Traditional Chinese Medicine-Refined Data Elements

For digitalization of traditional Chinese medicine(TCM),research is being conducted on objectivization of diagnosis and treatment,mathematical models of TCM theories,and application of modern information technology to digitize the vast amounts of existing information.However,the author believes that TCM practitioners should first conduct a systematic and comprehensive refined analysis on the knowledge of TCM and unify data elements used in computer intelligence to avoid ambiguity.Thus,we must overcome the epistemological constraints and carefully analyze the relationship among data elements to achieve systematic results and administer TCM appropriately.

Three-dimensional finite element analysis of hyperbaric oxygen therapy on the optic nerve from a damaged orbit

A middle-aged male patient with a right orbital comminuted fracture underwent computer tomography scanning, and a three-dimensional finite element model of the eyes and relevant tissues was established. Optic nerve stress in a hyperbaric oxygen environment was simulated and analyzed by changing the elastic modulus and external pressure of the skull at the damage side. Results showed that stress maximized at the contact site of the optic nerve and the eyeball in the damaged and intact eye orbits. Optic nerve stress at the damaged orbit significantly increased; however, stress in the intact orbit only slightly changed with decreased elastic modulus of the skull while external pressure remained unchanged. Maximum optic nerve stress increased in the damaged and intact side, along with increased external pressure, while elastic modulus remained unchanged. These experimental findings suggested that the optic nerve was compressed under hyperbaric oxygen and optic nerve stress was greater in the damaged orbit than in the intact orbit.

Precise location of proton of beta-secretase for catalytic aspartates(Asp 32 and Asp 228)in Alzheimer’s patients

BACKGROUND：β-secretase （β-site APP cleavage rate-limiting enzyme, BACE） has been proposed as a promising therapeutic target for Alzheimer＇s disease （AD）. BACE inhibition reduces production of β-amyloid peptide （Aβ） and promotes neural regeneration. Two catalytic aspartates （Asp 32 and Asp 228） exist in a monoprotonated state in the active BACE site, but the precise proton location remains unclear.OBJECTIVE：To explore the entire process of BACE enzymatic hydrolysis using quantum chemistry calculations, and to identify the precise proton location for Asp 32 and Asp 228 during the enzymatic process.DESIGN, TIME AND SETTING：According to protonation state of BACE, four tautomers were designed and quantum chemistry calculations were performed at the Department of Human Anatomy, Zhongshan School of Medicine, Sun Yat-sen University, China between October 2008 and March 2009.MATERIALS：Hardware：linux workstation （Department of Equipment, Sun Yat-sen University, China）; software：QSITE, Glide, Maestro （Schrodinger LLC, USA）, MOPAC 2007 （CAChe Research LLC, USA）, Triton 4.0 （National Centre for Biomolecular Research, Czech Republic） were used.METHODS：Using crystal structures of BACE to build a catalytic model （enzyme, catalytic water, and substrate peptide EVNLAAEF） on the computer and superimposition, four BACE tautomers （32i, 320, 228i, and 2280） in the monoprotonated state were developed with Schrodinger package. Hybrid quantum mechanical/molecular mechanic （QM/MM） calculations were performed at the B3LYP density functional theory level to identify the precise proton location for the dyad aspartic residues （Asp 32 and Asp 228）. Using the most possible tautomer as the reactant, the entire enzymatic hydrolysis of substrate EVNL/AAEF was simulated at the semiempirical level.MAIN OUTCOME MEASURES：The precise proton location of was measured by analyzing co-planarities of 4 BACE tautorners （32i, 32o, 228i, and 2280） in the monoprotonated state, because the dihedral formed by the carboxyl oxygen atoms of the dyad aspartic residues. The transition state and the production state, as well as activation energies and reaction enthalpies, were measured by calculating geometric and energy changes during catalytic reaction of the system.RESULTS：In the 2280 BACE tautomer, the dihedral angle of the four oxygen atoms in the catalytic aspartates was 8.7°, which was the lowest of four tautomers. The lowest activation energy and highest reaction enthalpy （Ea = 216.30 kJ/mol, AH = 30.98 kJ/mol） were also found in 2280, among the four tautomers during the catalytic reaction. In addition, when the reaction proceeded to the transition state, followed by product generation, the proton location was reversed to the inner oxygen of Asp 32 （32i） from the outer oxygen of Asp 228 （228o）.CONCLUSION：Results demonstrated the mechanism of Aβ generation. At beginning of BACE catalytic reaction, the precise proton location was preferred on the outer oxygen of Asp 228 （228o）. In this protonation state, catalytic reaction can proceed smoothly, with reduced active energy and heat release. When the reaction proceeded to the transition state and product generation, the proton location was reversed to the inner oxygen of Asp 32 （32i）. These results provide theoretical guidance for designing new drugs to protect neural cells and promote neural regeneration in Alzheimer＇s patients.

Augmented reality(AR)and fracture mapping model on middle-aged femoral neck fracture:A proof-of-concept towards interactive visualization

A thorough understanding of the fracture characteristics can assist the decision-making process for surgery.This study aimed to characterize the femoral neck fractures among middle-aged patients and illustrated a biomedical visualization method using a fracture mapping model and augmented reality.We collected plain radiography and computed tomography(CT)data from 156 adult patients with a femoral neck fracture.The descriptive study showed that Type I and Type II fractures accounted for 8(5%)and 64(41%)cases.In comparison,Type IV fractures accounted for 44(28%)and 40(25%)cases according to the Garden classification.Comminuted fractures and cortical defects were identified in 14.74%and 29.49%of the cases.A fracture mapping model was reconstructed based on the CT data and demonstrated the location and distribution of the major fracture lines surrounding the head-neck junction.We also illustrated the application of augmented reality technology to visualize and interact with the patient-specific fracture model and the fracture mapping model that facilitated education,training,and surgical planning.Future studies may consider mapping other biomechanical data,such as joint loading and stress distribution,and exploring artificial intelligence via deep reinforcement learning for computer-aided fracture reduction and procedure planning.