36results about How to "Improve long-term survival" patented technology

The invention discloses a deep convolutional neural network-based lung cancer preventing self-service health cloud service system. The system comprises a convolutional neural network used for deep learning and training identification, a segmentation module which segments out a lung region from a CT image based on a full convolutional neural network, a deep convolutional neural network used for lung cancer diagnosis classification, and a self-service health cloud service platform used for performing early prevention and treatment according to an identified suspected lung cancer type. According to the system, the automation and intelligentization level of mobile internet-based lung cancer screening can be effectively improved, more citizens can know and participate in self-service health detection, assessment and guidance, the sensitivity, specificity and accuracy of early lung cancer screening and clinical diagnosis are improved, the lung cancer can be early discovered, early diagnosed and early treated, and the self-health management capability is enhanced.

The invention relates to a drug composition used for preventing hypertension complications and an application thereof. The drug composition provided by the invention is prepared from the following active ingredients in parts by weight: 22-28 parts of szechuan lovage rhizome, 15-20 parts of herba epimedii, 15-20 parts of rhizoma alismatis, 12-16 parts of radix angelicae pubescentis, 12-16 parts of mother chrysanthemum, 12-18 parts of polygala tenuifolia, 10-15 parts of semen plantaginis, 10-15 parts of Sichuan radix curcumae and 8-12 parts of grape seed. The drug composition provided by the invention can obviously reduce systolic pressure of a patient suffering from primary hypertension, effectively prevent various complications of a hypertensive patient and increase long-term survival rate of the patient.

The invention discloses a dry weight auxiliary adjustment system for hemodialysis patients based on deep reinforcement learning. The system includes a data acquisition module, a data processing module, a strategy learning module and an auxiliary decision-making module; the invention utilizes the deep reinforcement learning technology to construct competitive The deep double-Q network (Dueling DDQN network) of the framework is used as an agent to simulate the process of doctors adjusting the dry weight of hemodialysis patients, and intelligently learn the strategy of adjusting the dry weight of hemodialysis patients. The invention models the dry weight regulation process of hemodialysis patients as a partially observed Markov process, defines respective state space and action space for different dialysis periods, and designs a reward function including long-term survival reward and short-term dialysis side reaction penalty ; Through the interactive learning between the agent and the patient's state, the dry weight adjustment strategy that maximizes the overall reward is obtained, thereby assisting the doctor in the long-term management of the patient's dry weight.

The invention discloses a double-layer nasal mask which comprises an inner layer which can be attached on the face of a patient to cover the nose part, and an outer layer which is arranged outside theinner layer; the inner layer is provided with an air inlet channel; an air chamber is formed between the inner layer and the nose part; an air exhaust channel is formed between the inner layer and the outer layer; the air chamber and the air exhaust channel are connected by a one-way air valve; the air flows unidirectionally from the air chamber to the air exhaust channel; and at least part of the periphery of the inner layer is folded inwards so as to form an inner-layer crease which can be tightly attached on the face of the patient to play the sealing role. The sealed belt formed by the sticking between the inner-layer crease and the face of the patient causes the nasal mask to have obviously improved sealing effect so as to prevent air form leakage, thus avoiding undesirable impact onthe patient due to the leakage of the air, and improving the curative effect.

The invention discloses a medicine for treating esophageal cancer. The medicine comprises the following raw materials in part by weight: 9 to 15 parts of astragalus, 10 to 15 parts of tangerine peel, 8 to 13 parts of cinnamomvine, 16 to 20 parts of stir-fried chicken's gizzard-membrane with vingar, 8 to 12 parts of muscone, 6 to 10 parts of scorpion, 5 to 8 parts of centipede, and 7 to 10 parts of borneol. Active pharmaceutical ingredients are prepared from the Chinese medicines, and capsule is prepared by mixing the active pharmaceutical ingredients and starch. The medicine can soothe liver and regulate qi, promote blood circulation and remove blood stasis, and clear heat and remove toxicity, has an anticancer and pain-relieving effect and does not have toxic or side effect; and clinical experiment proves that the medicine has a good effect of treating the esophageal cancer.

The present invention provides a bispecific chimeric antigen receptor combining two single chain antibodies, a coding gene, a combined expression vector, a method for preparing a recombinant lentivirus, a method for preparing CD3+T genetically modified by the bispecific chimeric antigen receptor combining the two single chain antibodies, and use of the genetically-modified CD3+T, and the bispecific chimeric antigen receptor combining the two single chain antibodies includes a CD8 signal peptide, an antigen binding domain composed of the two different single chain antibodies, a transmembrane domain, and an intracellular signaling domain. The treatment of patients with CD19 target loss recurrence and plasma cell tumor can be achieved, and a more effective treatment route is provided for tumor diseases.