Handheld hyperspectral imaging as a tool for the post-mortem interval estimation of human skeletal remains.

In forensic medicine, estimating human skeletal remains' post-mortem interval (PMI) can be challenging. Following death, bones undergo a series of chemical and physical transformations due to their interactions with the surrounding environment. Post-mortem changes have been assessed using various methods, but estimating the PMI of skeletal remains could still be improved. We propose a new methodology with handheld hyperspectral imaging (HSI) system based on the first results from 104 human skeletal remains with PMIs ranging between 1 day and 2000 years. To differentiate between forensic and archaeological bone material, the Convolutional Neural Network analyzed 65.000 distinct diagnostic spectra: the classification accuracy was 0.58, 0.62, 0.73, 0.81, and 0.98 for PMIs of 0 week-2 weeks, 2 weeks-6 months, 6 months-1 year, 1 year-10 years, and >100 years, respectively. In conclusion, HSI can be used in forensic medicine to distinguish bone materials >100 years old from those <10 years old with an accuracy of 98%. The model has adequate predictive performance, and handheld HSI could serve as a novel approach to objectively and accurately determine the PMI of human skeletal remains.

Post-Mortem Interval of Human Skeletal Remains Estimated with Handheld NIR Spectrometry.

Estimating the post-mortem interval (PMI) of human skeletal remains is a critical issue of forensic analysis, with important limitations such as sample preparation and practicability. In this work, NIR spectroscopy (NIRONE® Sensor X; Spectral Engines, 61449, Germany) was applied to estimate the PMI of 104 human bone samples between 1 day and 2000 years. Reflectance data were repeatedly collected from eight independent spectrometers between 1950 and 1550 nm with a spectral resolution of 14 nm and a step size of 2 nm, each from the external and internal bone. An Artificial Neural Network was used to analyze the 66,560 distinct diagnostic spectra, and clearly distinguished between forensic and archaeological bone material: the classification accuracies for PMIs of 0−2 weeks, 2 weeks−6 months, 6 months−1 year, 1 year−10 years, and >100 years were 0.90, 0.94, 0.94, 0.93, and 1.00, respectively. PMI of archaeological bones could be determined with an accuracy of 100%, demonstrating the adequate predictive performance of the model. Applying a handheld NIR spectrometer to estimate the PMI of human skeletal remains is rapid and extends the repertoire of forensic analyses as a distinct, novel approach.

Application of Micro-Computed Tomography for the Estimation of the Post-Mortem Interval of Human Skeletal Remains.

It is challenging to estimate the post-mortem interval (PMI) of skeletal remains within a forensic context. As a result of their interactions with the environment, bones undergo several chemical and physical changes after death. So far, multiple methods have been used to follow up on post-mortem changes. There is, however, no definitive way to estimate the PMI of skeletal remains. This research aimed to propose a methodology capable of estimating the PMI using micro-computed tomography measurements of 104 human skeletal remains with PMIs between one day and 2000 years. The present study indicates that micro-computed tomography could be considered an objective and precise method of PMI evaluation in forensic medicine. The measured parameters show a significant difference regarding the PMI for Cort Porosity p < 0.001, BV/TV p > 0.001, Mean1 p > 0.001 and Mean2 p > 0.005. Using a machine learning approach, the neural network showed an accuracy of 99% for distinguishing between samples with a PMI of less than 100 years and archaeological samples.

Nerve at risk: anatomical variations of the left recurrent laryngeal nerve and implications for thoracic surgeons.

OBJECTIVES: Recurrent laryngeal nerve (RLN) injury during thoracic surgery may result in life-threatening postoperative complications including recurrent aspiration and pneumonia. Anatomical details of the intrathoracic course are scarce. However, only an in-depth understanding of the anatomy will help reduce nerve injury. The aim of this study was to assess the anatomic variations of the intrathoracic left RLN. METHODS: Left-sided vagal nerves and RLN were dissected in 100 consecutive Caucasian cadavers during routine autopsy. Anatomical details were documented. Available demographic data were assessed for possible correlations. RESULTS: All nerves were identified during dissection. Variant courses were classified in 3 different groups according to the level at which the RLN separated from the vagal nerve: above the aortic arch, level with the aortic arch and below the aortic arch. We found 11% of RLN separating above the aortic arch and crossing the aortic arch at a considerable distance to the vagal nerve. In 48% of the RLN, the nerve split off when it was level with the aortic arch, and 41% of the RLN leave the vagal nerve in a perpendicular direction below the aortic arch. All nerves crossed the ligamentum arteriosum on the posterior side. No gender-specific differences were observed. CONCLUSIONS: Mediastinal lymph node dissection in left-sided lung cancer patients puts the RLN at risk. With more detailed anatomical knowledge about its course, it is possible to avoid risking the nerve. Visualization will help protect the nerve.

Time course of recovery after two successive night shifts: A diary study among Austrian nurses.

AIM: To explore how nurses recover from night shift work during periods off duty. BACKGROUND: Given the large number of affected individuals globally, detrimental health outcomes of night shift work are an important public health issue. Sufficient recovery opportunities are essential to prevent prolonged fatigue associated with demanding tasks and high workload as experienced in nurses working long shifts during the night. METHOD: Nurses (n = 53, 96% females) who worked in two public nursing homes in Austria completed a 5-day diary to collect data on well-being conceptualized by fatigue, distress and vigour. RESULTS: Nurses experienced worse well-being post night shift days than during rest days. Well-being improved from post night shift day 2 to the following rest day 1 and continued improvement from rest day 1 to rest day 2. CONCLUSIONS: Nurses who work at nights are at risk for experiencing prolonged fatigue. Our results suggest that after two consecutive 12-hr night shifts full recovery needs at least three days off work. IMPLICATIONS FOR NURSING MANAGEMENT: Strategies for maintaining nurses' good health and caring attitudes as well as vigilance for patient safety should include fatigue management plans and optimised schedules for night shift work.