Biomedical and Health Sciences
This article provides an introduction to the field of datasets, including their types, characteristics, and applications. Datasets refer to collections of data that have been organized for specific purposes. They can come in various forms, including structured data, unstructured data, and semi-structured data. Each type of dataset has its own unique characteristics and uses. For example, structured data typically includes datasets that have been organized into tables and rows, such as spreadsheets or databases, while unstructured data typically includes text, images, and videos.
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<p> The dataset is digital health data. It contains heart rate data extracted from Fitbit version 2 smartwatch worn by a healthy male Asian person of 48 years old. Data is of one-month duration. We have uploaded a zip file that contains data from different days. Data for each day has a separate file. The file name contains the date. Each file is in csv format. Each file has two columns – timestamp and heart rate. It is a continuous time-series heart rate data. Heart rate was recorded seamlessly at 5 sec interval. However, there may be missing datum.
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Objective: The current study investigates
whether, during a Cochlear Implant (CI) surgery, conditioning
(i.e. applying short bursts of electrical stimulation)
within a saline solution can have positive effects on subsequent
intra-operative measurements. We hypothesize that,
based on previous research, the impedance values will be
reduced, and that the reproducibility of Electrically Evoked
Compound Action Potentials (ECAPs) is improved as a
result of conditioning.
Methods: We conditioned half of the electrode contacts,
within a saline solution, before CI insertion, using 23 MEDEL
implants. Impedance was measured for both the conditioned
and non-conditioned groups at five time points.
Repeated ECAP recordings were measured and compared
between the conditioned and non-conditioned groups.
Results: Impedance of the electrode contacts were reduced
by 31% after conditioning in saline solution; however,
there were no clinically relevant differences after
the implantation of the electrode array. The hypothesis
that measurement reproducibility would be increased after
conditioning could not be confirmed with our data.
Within the saline solution, we observed that 44% of the
electrode contacts were covered with air bubbles, which
most disappeared after implantation. However, these air
bubbles limited the effectiveness of the conditioning within
the saline solution. Lastly, the effect of conditioning on the
reference electrode stimulation was approximately 16% of
the total reduction in impedance.
Conclusion: Our data does not suggest that intraoperative
conditioning is clinically required for cochlear implantation
with MED-EL implants. Additionally, an in-vivo ECAP
recording can be considered as a method of conditioning
the electrode contacts.
Significance: We confirm that the common clinical practice
does not need to be changed.
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Use of medical devices in the magnetic resonance environment is regulated by standards that include the ASTM-F2213 magnetically induced torque. This standard prescribes five tests. However, none can be directly applied to measure very low torques of slender lightweight devices such as needles. Methods: We present a variant of an ASTM torsional spring method that makes a “spring” of 2 strings that suspend the needle by its ends. The magnetically induced torque on the needle causes it to rotate. The strings tilt and lift the needle.
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The description of the proposed glomerulus segmentation dataset is as follows: The dataset contains 200 WSI (H&E, PAS, MAS, PASM) images provided by Peking University Shenzhen Hospital and manually labeled by pathologists. The slides of the dataset are basically similar in tissue structure but not pix-level paired. The magnification of the slides is 40×, and the resolution is 0.2528μm/pixel.
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Automatic extraction of valuable, structured evidence from the exponentially growing clinical trial literature can help physicians practice evidence-based medicine quickly and accurately. However, current research on evidence extraction has been limited by the lack of generalization ability on various clinical topics and the high cost of manual annotation. In this work, we address these challenges by constructing a PICO-based evidence dataset PICO-DS, covering five clinical topics.
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Problems related to ventral hernia are very common, and evaluating them using computational methods can assist in selecting the most appropriate treatment. This study collected data from over 3500 patients from different European countries observed during last 11 years (2012-2022), which were collected by specialists in hernia surgery. The majority of patients underwent standard surgical procedures, with a growing trend towards robotic surgery. This paper focuses on statistically evaluating the treatment methods in relation to patient age, body mass index (BMI), and the type of repair.
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This dataset is used to verify the effectiveness of the proposed MS-caCOH method.
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The dataset is a datasheet of the average angular velocity of the knee joint during walking at a speed of about 5km per hour. To obtain the dataset, we first conducted gait collection experiments with 137 healthy adults (71 males and 66 females, age: 21.6±1.8). None of the subjects had any history of neurological injury or gait disorder. The subjects were asked to walk at different speeds, and Gait patterns were recorded with a Vicon motion capture system with 10 infrared cameras and 4 force plates.
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