For the last 20 years, a great amount of evidence has accumulated through epidemiological studies that most of the dry eye disease encountered in daily life, especially in video display terminal (VDT) workers, involves short tear film breakup time (TFBUT) type dry eye, a category characterized by severe symptoms but minimal clinical signs other than short TFBUT. An unstable tear film also affects the visual function, possibly due to the increase of higher order aberrations. Based on the change in the understanding of the types, symptoms, and signs of dry eye disease, the Asia Dry Eye Society agreed to the following definition of dry eye: "Dry eye is a multifactorial disease characterized by unstable tear film causing a variety of symptoms and/or visual impairment, potentially accompanied by ocular surface damage." The definition stresses instability of the tear film as well as the importance of visual impairment, highlighting an essential role for TFBUT assessment. This paper discusses the concept of Tear Film Oriented Therapy (TFOT), which evolved from the definition of dry eye, emphasizing the importance of a stable tear film.

Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer’s disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer’s disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer’s disease and cognitively normal subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer’s Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer’s disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

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Acupuncture versus no treatment, and as an adjunct to conventional care, should be advocated in the European Guidelines for the treatment of chronic LBP.

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The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The worldwide shortage of pathologists offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate the workload and increase diagnostic accuracy. Here, we report a clinically applicable system developed at the Chinese PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-level annotated H&E-stained whole slide images. The model achieves a sensitivity near 100% and an average specificity of 80.6% on a real-world test dataset with 3,212 whole slide images digitalized by three scanners. We show that the system could aid pathologists in improving diagnostic accuracy and preventing misdiagnoses. Moreover, we demonstrate that our system performs robustly with 1,582 whole slide images from two other medical centres. Our study suggests the feasibility and benefits of using histopathological artificial intelligence assistance systems in routine practice scenarios.

Background: Rural township health centres and urban community health centres play a crucial role in the delivery of primary health care in China. Over the past two-and-a-half decades, these health institutions have not been as well developed as high-level hospitals. The limited availability and low qualifications of human resources in health are among the main challenges facing lowerlevel health facilities. This paper aims to analyse the mobility of health workers in township and community health centres.

These authors contributed equally to this work. ⇤ Corresponding authors.Gastric cancer is among the malignant tumors with the highest incidence and mortality rates.Early detection and accurate histopathological diagnosis of gastric cancer are essential factors that can help increase the chances of successful treatment. While the worldwide shortage of pathologists has imposed burdens on the current histopathology service, it also offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate the workload and increase diagnostic accuracy. To the best of our knowledge, there has not been a clinically applicable histopathological assistance system with high accuracy, and can generalize to whole slide images created with diverse digital scanner models from different hospitals. Here, we report the clinically applicable artificial intelligence assistance system developed at the Chinese PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-level annotated whole slide images. The model achieved a sensitivity near 100% and an average specificity of 80.6% on a real world test dataset, which included 3,212 whole slide images digitalized with three scanner models. We showed that the system would aid pathologists in improving diagnostic accuracy and preventing misdiagnosis. Moreover, we demonstrated that our system could perform robustly with 1,582 whole slide images from two other medical centers. Our study proves the feasibility and benefits of using histopathological artificial intelligence assistance systems in routine practice scenarios.Gastric cancer is the fifth most common cancer worldwide and the third leading cause of cancer death 1 . There is a wide geographic difference in its incidence, with the highest incidence rate in East Asian populations 2 . In China, about 498,000 new cases were diagnosed in 2015, which was the second leading cause of cancer-associated mortality 3 . As early detection, accurate diagnosis and surgical intervention are crucial factors to reduce gastric cancer mortality, robust