Comorbidity risk and distribution characteristics of chronic diseases in the elderly population in China.

BACKGROUND: The risk of comorbid chronic diseases in elderly people is an important problem affecting their health and quality of life. We analyzed the incidence of chronic diseases for combinations of chronic diseases analyzed. METHODS: We used the original data to construct hypothetical cohorts of elderly individuals that evolved with age. The complex network was used to reduce the dimensionality of disease. The multistate transition model is used to calculate the incidence of each chronic disease, exploring comorbidity characteristics and rules. RESULTS: (1) By using complex network, seven chronic diseases were screened out in men, including hypertension, diabetes, heart disease, stroke, chronic lung disease, arthritis and dyslipidemia; six chronic diseases in women showed significant comorbidity except chronic lung disease. (2) Incidence show differences in age and sex; incidence of chronic diseases generally increased with age. (3) The marginal risk increases with the number of basic chronic diseases associated with comorbidities. (4) When hypertension is present as a basic disease, its impact on the risk of other chronic diseases is much less than that of other chronic diseases. (5) When diseases occur as basic chronic diseases, hypertension-heart disease and diabetes-dyslipidemia are combinations that have the greatest impact on each other in men; hypertension-heart disease in women. CONCLUSIONS: The incidence of chronic diseases in patients who have chronic diseases and will form comorbidities differs from that in healthy states, and the related effects of different chronic diseases also differ. Among these conditions, hypertension is caused by a special mechanism.

PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data.

Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies and addressed the privacy concern in three major tasks of omic data under three representative deep learning models. We examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients' privacy in omic data.

Highly sensitive vector bending sensor based on an embedded multimode D-shaped LPFG.

In this letter, a highly sensitive bending sensor based on an embedded multimode D-shaped long period fiber grating (EMD-LPFG) is proposed. The novel sensor is applied to carry out vector bending measurement. The proposed LPFG is fabricated by polishing on the prepared structure which is formed by periodically splicing between single mode fiber (SMF) and multimode fiber (MMF). Since the cross section of the embedded MMF is D-shaped, we named it EMD-LPFG. Due to the asymmetric modulation of the refractive index on the fiber by the CO2 laser, the sensor has the ability to distinguish the bending directions, and the MMFs provide higher bending response. The experimental transmission spectrum can match the simulation results well. The experimental results show that the average bending sensitivities in three orthogonal directions are 70.21 nm/m-1 (0°), 9.75 nm/m-1 (90°), -12.04 nm/m-1 (180°) and 9.98 nm/m-1 (270°), respectively. Meanwhile, the temperature sensitivity is 30 pm/°C in the range of 25 °C to 75 °C. According to the ultra-compact structure with the total length of 2.5 mm, high bending sensitivity and ability to distinguish the bending direction, the novel sensor has potential in bending measurement.