11 results about "Non diabetic" patented technology

The present application belongs to the technical field of artificial intelligence prediction of glucose-metabolism-related diseases, and relates to a method for constructing a glucose-metabolism-related disease prediction model, a method for constructing a non-diabetic subtyping model, and a method for predicating postprandial blood glucose. In the present application, large-scale unlabeled dynamic blood glucose concentration data is used to construct a pre-trained model, and the pre-trained model is fine-tuned on the basis of clinical diagnosis and clinical physiological data, thereby realizing the prediction of glucose-metabolism-related diseases. Secondly, in the present application, dynamic blood glucose feature data is captured on the basis of large-scale CGM data, and non-diabetic dynamic blood glucose distribution data is obtained by means of clustering, thereby realizing the prediction of non-diabetic subtypes of individuals by using individual CGM data and the non-diabetic dynamic blood glucose distribution data. Furthermore, in the present application, individual dynamic blood glucose data obtained by means of continuous glucose monitoring is encoded and embedded into a latent space by means of the pre-trained model to obtain low-dimensional dynamic blood glucose feature data, such that predicted postprandial blood glucose corresponding to dietary structure information can be obtained on the basis of preprandial immediate blood glucose data and the dietary structure information.

A method for detecting and treating Type II Diabetes includes the use of hsa_piR_020485, a PIWI-interacting RNA isolated from urinary extracellular vesicles (ECVs), as a biomarker for the diagnosis and treatment of Type 2 diabetes mellitus (T2DM). The method is non-invasive, utilizing the differential expression of hsa_piR_020485 in diabetic versus non-diabetic subjects to identify subjects in need of treatment for T2DM. The method includes obtaining a urine sample from a subject, isolating urinary ECVs from the urine sample, extracting total RNA from the isolated urinary ECVs, quantifying hsa_piR_020485 levels from the total RNA, determining if the hsa_piR_020485 expression level exceeds a threshold, and administering one or more T2DM treatments.

Provided herein, in one aspect, is a method of preventing or delaying the onset of clinical type 1 diabetes (TID), comprising: providing a non-diabetic subject who is at risk for TID; determining that the non-diabetic subject (1) is substantially free of antibodies against zinc transporter 8 (ZnT8), (2) is HLA-DR4+, and / or (3) is not HLA-DR3+; and administering a prophylactically effective amount of an anti-CD3 antibody to the non-diabetic subject.

The disclosure provides at least one compound, tautomer, deuterated derivative, or pharmaceutically acceptable salt chosen from compounds of Formula I, tautomers thereof, deuterated derivatives of those compounds or tautomers, and pharmaceutically acceptable salts of any of the foregoing, compositions comprising the same, and methods of using the same, including uses in treating APOL1-mediated diseases, including pancreatic cancer, focal segmental glomerulosclerosis (FSGS), and / or non-diabetic kidney disease (NDKD).

The present application belongs to the technical field of artificial intelligence prediction of glucose-metabolism-related diseases, and relates to a method for constructing a glucose-metabolism-related disease prediction model, a method for constructing a non-diabetic subtyping model, and a method for predicating postprandial blood glucose. In the present application, large-scale unlabeled dynamic blood glucose concentration data is used to construct a pre-trained model, and the pre-trained model is fine-tuned on the basis of clinical diagnosis and clinical physiological data, thereby realizing the prediction of glucose-metabolism-related diseases. Secondly, in the present application, dynamic blood glucose feature data is captured on the basis of large-scale CGM data, and non-diabetic dynamic blood glucose distribution data is obtained by means of clustering, thereby realizing the prediction of non-diabetic subtypes of individuals by using individual CGM data and the non-diabetic dynamic blood glucose distribution data. Furthermore, in the present application, individual dynamic blood glucose data obtained by means of continuous glucose monitoring is encoded and embedded into a latent space by means of the pre-trained model to obtain low-dimensional dynamic blood glucose feature data, such that predicted postprandial blood glucose corresponding to dietary structure information can be obtained on the basis of preprandial immediate blood glucose data and the dietary structure information.

The invention relates to the technical field of diabetes risk assessment, and discloses a diabetes risk assessment method and system, and the method comprises the steps: collecting user data, obtaining a diabetes diagnosis standard, and carrying out the judgment of a crowd according to the user data and the diabetes diagnosis standard; establishing a life correction model, a saccharification deviation model and a glycosylation accumulation model, and processing the user data to obtain analysis data; when the crowd is judged to be a non-diabetic crowd, establishing a disease assessment model, calculating a basic risk score by the disease assessment model based on a life correction model, auditing and correcting the basic risk score through a disease condition to obtain a glycosylation correction risk score, and mapping the glycosylation correction risk score into a disease risk level; and when the crowd is determined to be the diabetic crowd, establishing a control evaluation model, calculating a basic control risk level by the control evaluation model based on the life correction model and the glycosylation accumulation model, and auditing and correcting the basic control risk level through a control condition to obtain the control risk level.

Chronic wounds are characterized by a persistent hyperinflammatory environment that inhibits progression toward regenerative wound closure. Such chronic wounds are particularly prevalent in diabetic patients, often necessitating distal limb amputation, but they also occur in non-diabetic and elderly patients. Inducible expression of HoxA3, a homeobox family member and master regulatory transcription factor for body patterning, has been shown to promote wound closure in diabetic mice when administered topically as a plasmid encapsulated in a hydrogel. Here, we provide independent reproducibility of basal in vivo studies of diabetic wound closure and further expand on these studies while minimizing the estimated minimum dose threshold. Furthermore, we observed similarities in spontaneous wound healing rates between non-diabetic aged mice and diabetic young mice, which motivated us to test the topical application of HoxA3 plasmids in non-diabetic aged mice, where accelerated wound healing was also observed. In these short studies, no significant adverse effects were observed macroscopically or by local histology. Local administration of HoxA3, either as a plasmid or as a future alternative therapy, is a noteworthy translational option for chronic wounds.

The present disclosure relates to methods for treating or preventing obesity and other metabolic disorders in diabetic and non-diabetic subjects using combination therapy comprising a therapeutically effective dose of an antagonistic antigen binding molecule that specifically binds to the human glucagon receptor (GCGR) co-administered with a therapeutically effective low dose of glucagon like peptide- 1 receptor (GLP-1 R) agonist peptide approved for treating T2DM or using a bifunctional fusion molecule comprising a glucagon like peptide- 1 receptor (GLP-1 R) agonist peptide fused directly to an antagonistic antigen binding molecule that specifically binds to the human glucagon receptor (GCGR).

The present application relates to the technical field of diabetes risk assessment, and discloses a diabetes risk assessment method and system, comprising: collecting user data, obtaining a diabetes diagnosis standard, and performing crowd determination according to the user data and the diabetes diagnosis standard; establishing a life correction model, a glycation deviation model and a glycosylation accumulation model, processing the user data to obtain analysis data; when the crowd determination is a non-diabetes crowd, establishing an onset evaluation model, the onset evaluation model calculating a basic risk score based on the life correction model, correcting the basic risk score through onset condition auditing to obtain a glycosylation corrected risk score, and mapping the glycosylation corrected risk score to an onset risk grade; when the crowd determination is a diabetes crowd, establishing a control evaluation model, the control evaluation model calculating a basic control risk grade based on the life correction model and the glycosylation accumulation model, and correcting the basic control risk grade through control condition auditing to obtain a control risk grade.

The invention belongs to the technical field of medical data processing, and discloses a collaborative generation method and system for a diabetes risk value assessment and intervention scheme, and the method comprises the steps: obtaining the five-mode data of the physiology, life behavior, heredity, environment and medical service utilization of a non-diabetic population; adopting a space-time attention mechanism to mine dynamic association between modes to generate dynamic interaction features; based on a preset crowd / scene dimension, utilizing the contribution degree and the attention weight to adaptively screen out a simplified feature set; a multi-task collaborative integrated prediction framework with diabetes attack risk prediction as a main task and intervention response prediction as an auxiliary task is constructed, a prediction result is weighted based on a three-dimensional dynamic weight calculated based on modal contribution degree, feature importance and task loss, and an attack risk value and an optimal intervention scheme are output. According to the invention, through dynamic mining of multi-modal data interaction, adaptive screening of features and linkage prediction of risk and intervention schemes, the prediction accuracy is improved.

To provide a method for preventing or delaying the onset of clinical type 1 diabetes (T1D).SOLUTION: Providing a non-diabetic subject at risk of T1D, administering to the non-diabetic subject a prophylactically effective amount of anti- CD3 antibodies, and prior to or subsequent to the administering step, determining that the non-diabetic subject has greater than about 5% to greater than about 10% TIGIT + T1D + CD3 + T cells among total KLRG1 + T cells, indicating successful prevention or delay of the onset of clinical CD8.SELECTED DRAWING: None