Alzheimer's disease (AD) patients experience declining memory, anxiety, and depression along with a decrease in adult hippocampal neurogenesis (AHN). It's still unclear if improving AHN in the damaged AD brain will improve affective and cognitive performance. According to earlier studies, in two different AD mice models, 5×FAD and 3×Tg-AD, patterned optogenetic activation of the hypothalamic supramammillary nucleus (SuM) increases AHN. Interestingly, these AD mice's memory and emotional deficiencies are restored when SuM-enhanced adult-born neurons (ABNs) are chemogenetic activated. In contrast, behavioral impairments cannot be restored by SuM stimulation alone or by activating ABNs without modifying SuM. This paper will design 3 experiments to explore the mechanism of activating SuM modified ABN to improve memory and emotional function in AD, including who enhances cognitive and emotional function, the activition of microglia and the long-term effect of SuM modified ABNs on memory and emotion function of AD mice.

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In the past few decades, with the gradual maturation of robotics, especially in medical applications, tactile sensors based on various sensing principles have been developed. A multitude of technical solutions have been employed to design tactile sensors. Notably, microfabrication-based approaches possess several appealing features. Microfabrication technology enables the development of miniature sensors with excellent performance, which exhibit outstanding metrological characteristics, such as high precision, sensitivity, low power consumption, and favorable frequency response. The compact size and superior metrological properties enhance the potential role of tactile sensors in the medical field, particularly tactile resonance sensors (TRS). Its principle involves measuring the frequency shift △f, defined as the difference between the resonance frequency of the freely vibrating sensor and the resonance frequency measured when the sensor encounters an object. Therefore, △ f is related to the acoustic impedance of the object and can be utilized to characterize its material properties. In the medical domain, tactile resonance sensor systems have been developed for various applications, including cancer detection, assessment of human egg fertility, measurement of intraocular pressure, and diagnosis of edema. In this review, we mainly explore the basic principles, major medical applications, technological advancements, and challenges of TRS. TRS shows remarkable potential in medical application. Although innovations in sensor design and materials have expanded its functions, issues such as signal interference, lack of standardization, and insufficient clinical verification still need to be addressed. Overcoming these challenges is essential for realizing the full potential of TRS in improving medical diagnosis and patient care.

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Due to the increase in the number of early cardiac groups in today's society, there is no complete screening and rehabilitation process for early cardiac groups at the social level. This paper mainly studies the whole process of screening methods and sports rehabilitation methods to improve the quality of life of patients and help the body recover. This article mainly summarizes and reviews the relevant papers in the past and concludes that the screening of early heart disease requires a variety of means of synchronous cooperation. Holter, electrocardiogram, B-ultrasound and exercise electrocardiogram are synchronously coordinated to determine the risk of premature heartbeat. In cooperation with medium- and low-intensity aerobic training and strength training, physical recovery and improvement of heart function. As a relatively common heart disease, the popularization of the perception of the disease should not only be at the doctor's level, but should also make patients correctly recognize the means of rehabilitation and recovery of the disease, so that patients can reduce psychological pressure and face it positively. Having a correct and complete understanding of the disease can also help the body recover.

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This paper studies the research achievements of artificial intelligence (AI) and machine learning in Weight Loss in the past decade and analyzes the impacts and application prospects of AI and machine learning on Weight Loss. Obesity has become the most significant threat to human health. It is estimated that the global number of obese people will exceed 2.16 billion in 2023. AI and machine learning can analyze a large amount of complex data (including genetic, gene expression, metabolic, gut microbiota, hormonal, dietary, behavioral, and environmental factors, etc.) more efficiently and accurately. They have opened new avenues in areas such as analyzing the causes of obesity, predicting obesity risks, diagnosing obesity and determining its subtypes, providing personalized and precise nutrition plans, and offering psychological support, making it possible to address the weight loss issues of such a large-scale population. This paper systematically analyzes the integration of relevant scientific research achievements with various links in Weight Loss, exploring the direction for the further practical and commercial transformation of scientific research achievements. At the same time, based on the urgent pain points in Weight Loss applications, it analyzes the current research gaps and deficiencies and proposes suggestions for future scientific research directions.

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