Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Effects of dexamethasone and progesterone treatment on experimental brain injury induced by surgical electrocautery

Haemostasis is an important aspect in neurosurgical operations for the achievement of good outcome. Bipolar coagulation is an extensively used haemostatic technique in modern neurosurgery but it may also cause iatrogenic brain trauma due to thermal injury. Published studies on coagulation-induced brain injury on a histological level are, however, limited. The present study aimed at investigating the extent of inflammatory and glial responses caused by different settings of bipolar coagulation using an animal model. It also investigated whether and how pre-operative treatment with dexamethasone or progesterone, both known to have neuro-protective effects, would modulate gliosis and macrophage infiltration induced by bipolar coagulation. The study consisted of two parts. The first part investigated the astrocytic and macrophage responses after bipolar coagulation at different power settings. 45 Sprague-Dawley rats received craniotomy, followed by bipolar coagulation at different power output settings (mock operation as control, 20W and 40W) over the rat cortex for a standardized duration of two seconds. On day 3, day 7 and day 28, brain sections were assessed by immunohistochemical staining for GFAP (astrocytes) and ED1 (macrophages). Quantification of outcome by random field cell counting under light microscopy was performed. The second part of the study used another 45 male Sprague-Dawley rats, divided into three treatment groups: Group 1 received the vehicle agents only (Control); Group 2 received progesterone 20mg/kg; Group 3 received dexamethasone 1 mg/kg. All treatments were given intraperitoneally two hours before craniotomy. The animals received bipolar coagulation at 40W for a standardized duration of two seconds. On day 1, 3 and 7, brain sections were assessed by immunohistochemical staining for GFAP and ED1. Quantification of outcome by random field cell counting under light microscopy was performed. T2-weighted magnetic resonance imaging for the animals on day 3 was also performed. The results showed that bipolar coagulation was associated with significant glial and inflammatory responses that correlated with power output. Progesterone and dexamethasone were both effective in reducing the glial hypertrophy and macrophage infiltration associated with bipolar coagulation. Dexamethasone had an additional advantage of reducing brain oedema and cavity formation. The findings suggested that progesterone and dexamethasone could be further explored as potential protective and/or remedial agents for bipolar coagulation-induced brain trauma sustained during neurosurgical procedures.published_or_final_versionSurgeryMasterMaster of Research in Medicin

Facet-Dependent Catalytic Activity of Gold Nanocubes, Octahedra, and Rhombic Dodecahedra toward 4‑Nitroaniline Reduction

In this study, cubic, octahedral, and rhombic dodecahedral gold nanocrystals synthesized by a seed-mediated growth method were employed as catalysts for the examination of facet-dependent catalytic activity toward NaBH₄ reduction of <i>p</i>-nitroaniline to <i>p</i>-phenylenediamine at different temperatures. Different amounts of the nanocrystal solutions were used so that all samples contain particles with the same total surface area. UV–vis absorption spectra monitored the reaction progress. Rhombic dodecahedra showed the best catalytic efficiency at all the temperatures examined. Nanocubes have higher reaction rates than those of octahedra from 25 to 36 °C, so the catalytic activity for the reduction reaction follows the order of {110} > {100} > {111}. However, the reaction rates for octahedra increase rapidly with rising temperature; their reaction rate surpasses that for the nanocubes at 40 °C. Rate constants and activation energies were determined, again showing that the activation energy is lowest for rhombic dodecahedra. Density functional theory (DFT) calculations indicate highest binding energy between <i>p</i>-nitroaniline and the Au(110) plane. The results reveal rhombic dodecahedral gold nanocrystals as highly efficient catalysts

Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients

Abstract Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration